REFERENCES.bib

% This file was created with JabRef 2.10.
% Encoding: UTF8


@Article{Aguirre2007,
  Title                    = {Canine and human visual cortex intact and responsive despite early retinal blindness from RPE65 mutation.},
  Author                   = {Geoffrey K Aguirre and Andres M Komeromy and Artur V Cideciyan and David H Brainard and Tomas S Aleman and Alejandro J Roman and Brian B Avants and James C Gee and Marc Korczykowski and William W Hauswirth and Gregory M Acland and Gustavo D Aguirre and Samuel G Jacobson},
  Journal                  = {PLoS Med},
  Year                     = {2007},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {e230},
  Volume                   = {4},

  Abstract                 = {BACKGROUND: RPE65 is an essential molecule in the retinoid-visual cycle, and RPE65 gene mutations cause the congenital human blindness known as Leber congenital amaurosis (LCA). Somatic gene therapy delivered to the retina of blind dogs with an RPE65 mutation dramatically restores retinal physiology and has sparked international interest in human treatment trials for this incurable disease. An unanswered question is how the visual cortex responds after prolonged sensory deprivation from retinal dysfunction. We therefore studied the cortex of RPE65-mutant dogs before and after retinal gene therapy. Then, we inquired whether there is visual pathway integrity and responsivity in adult humans with LCA due to RPE65 mutations (RPE65-LCA). METHODS AND FINDINGS: RPE65-mutant dogs were studied with fMRI. Prior to therapy, retinal and subcortical responses to light were markedly diminished, and there were minimal cortical responses within the primary visual areas of the lateral gyrus (activation amplitude mean +/- standard deviation [SD] = 0.07\% +/- 0.06\% and volume = 1.3 +/- 0.6 cm(3)). Following therapy, retinal and subcortical response restoration was accompanied by increased amplitude (0.18\% +/- 0.06\%) and volume (8.2 +/- 0.8 cm(3)) of activation within the lateral gyrus (p < 0.005 for both). Cortical recovery occurred rapidly (within a month of treatment) and was persistent (as long as 2.5 y after treatment). Recovery was present even when treatment was provided as late as 1-4 y of age. Human RPE65-LCA patients (ages 18-23 y) were studied with structural magnetic resonance imaging. Optic nerve diameter (3.2 +/- 0.5 mm) was within the normal range (3.2 +/- 0.3 mm), and occipital cortical white matter density as judged by voxel-based morphometry was slightly but significantly altered (1.3 SD below control average, p = 0.005). Functional magnetic resonance imaging in human RPE65-LCA patients revealed cortical responses with a markedly diminished activation volume (8.8 +/- 1.2 cm(3)) compared to controls (29.7 +/- 8.3 cm(3), p < 0.001) when stimulated with lower intensity light. Unexpectedly, cortical response volume (41.2 +/- 11.1 cm(3)) was comparable to normal (48.8 +/- 3.1 cm(3), p = 0.2) with higher intensity light stimulation. CONCLUSIONS: Visual cortical responses dramatically improve after retinal gene therapy in the canine model of RPE65-LCA. Human RPE65-LCA patients have preserved visual pathway anatomy and detectable cortical activation despite limited visual experience. Taken together, the results support the potential for human visual benefit from retinal therapies currently being aimed at restoring vision to the congenitally blind with genetic retinal disease.},
  Bdsk-url-1               = {http://dx.doi.org/10.1371/journal.pmed.0040230},
  Doi                      = {10.1371/journal.pmed.0040230},
  Institution              = {Department of Neurology, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America. aguirreg@mail.med.upenn.edu},
  Keywords                 = {Adolescent; Adult; Animals; Blindness; Brain; Ca; Disease Models, Animal; Dogs; Eye Diseases, Hereditary; Eye Proteins; Female; Gene Therapy; Humans; Magnetic Resonance Imagin; Male; Mutation; Pigment Epithelium of Eye; Retina; Retinitis Pigmentosa; Visual Cortex; g; rrier Proteins},
  Owner                    = {stnava},
  Pii                      = {06-PLME-RA-1021},
  Pmid                     = {17594175},
  Timestamp                = {2008.05.29},
  Url                      = {http://dx.doi.org/10.1371/journal.pmed.0040230}
}

@InCollection{Arnold2014,
  Title                    = {Topological methods in hydrodynamics},
  Author                   = {Arnold, Vladimir I},
  Booktitle                = {Vladimir I. Arnold-Collected Works},
  Publisher                = {Springer},
  Year                     = {2014},
  Pages                    = {433--454},
  Owner                    = {stnava},
  Timestamp                = {2014.08.26},
  Url                      = {http://link.springer.com/chapter/10.1007/978-3-642-31031-7_42}
}

@Article{Ashburner2012,
  Title                    = {SPM: a history.},
  Author                   = {Ashburner, John},
  Journal                  = {Neuroimage},
  Year                     = {2012},

  Month                    = {Aug},
  Number                   = {2},
  Pages                    = {791--800},
  Volume                   = {62},
  Abstract                 = {Karl Friston began the SPM project around 1991. The rest is history.},
  Doi                      = {10.1016/j.neuroimage.2011.10.025},
  Institution              = {Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London WC1N 3BG, UK. john@fil.ion.ucl.ac.uk},
  Keywords                 = {Brain Mapping, history/methods; Brain, anatomy /&/ histology/physiology; History, 20th Century; History, 21st Century; Humans; Image Processing, Computer-Assisted, history/methods; Magnetic Resonance Imaging, history/methods; Software, history},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(11)01188-8},
  Pmid                     = {22023741},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2011.10.025}
}

@PhdThesis{Avants2005,
  Title                    = {Shape optimizing diffeomorphisms for medical image analysis},
  Author                   = {B. Avants},
  School                   = {University of Pennsylvania},
  Year                     = {2005}
}

@InProceedings{Avants2005a,
  Title                    = {Unbiased Diffeomorphic Shape and Intensity Atlas Creation},
  Author                   = {B. Avants and J. Aguirre and J. Walker and J. C. Gee},
  Booktitle                = {ISMRM},
  Year                     = {2005}
}

@Conference{Avants2007,
  Title                    = {Tauopathic Longitudinal Gray Matter Atrophy Predicts Declining Verbal Fluency: A Symmetric Normalization Study},
  Author                   = {B. Avants and C. Anderson and M. Grossman},
  Booktitle                = {Human Brain Mapping},
  Year                     = {2007},

  Owner                    = {stnava},
  Timestamp                = {2007.02.23}
}

@Article{Avants2007a,
  Title                    = {Spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia.},
  Author                   = {Brian Avants and Chivon Anderson and Murray Grossman and James C Gee},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2007},
  Number                   = {Pt 2},
  Pages                    = {303--310},
  Volume                   = {10},

  Abstract                 = {We present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. Our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual's image time series. The second step involves mapping a representative image or set of images from the time series into an optimal template space. The template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. Here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (FTD). We sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an annualized atrophy map (AAM) that estimates the annual effect of FTD. This spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. We found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. We also found significant effects in the hippocampus, insula and cingulate gyrus. Our novel results, significant at p < 0.05 after false discovery rate correction, are represented in local template space but also assigned Talairach coordinates and Brodmann and Anatomical Automatic Labeling (AAL) labels. This paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.},
  Institution              = {Dept. of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Alg; Atrophy; Cerebral Cortex; Dementia; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Longitudinal Studies; Magnetic Resonance Imaging; Neurons; Reproducibility of Results; Sensitivity and Specificity; orithms},
  Owner                    = {stnava},
  Pmid                     = {18044582},
  Timestamp                = {2008.02.25}
}

@Conference{Avants2007b,
  Title                    = {Symmetric normalization for patient-specific tracking of longitudinal change in frontotemporal dementia},
  Author                   = {B. Avants and C. Anderson and M. Grossman and J. C. Gee},
  Booktitle                = {Medical Image Computing and Computer Aided Intervention},
  Year                     = {2007},
  Pages                    = {303-310},
  Volume                   = {2},

  Owner                    = {stnava},
  Timestamp                = {2007.05.01}
}

@Article{Avants2010,
  Title                    = {Sparse unbiased analysis of anatomical variance in longitudinal imaging.},
  Author                   = {Avants, Brian and Cook, Philip A. and McMillan, Corey and Grossman, Murray and Tustison, Nicholas J. and Zheng, Yuanjie and Gee, James C.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2010},
  Number                   = {Pt 1},
  Pages                    = {324--331},
  Volume                   = {13},

  Abstract                 = {We present a new algorithm for reliable, unbiased, multivariate longitudinal analysis of cortical and white matter atrophy rates with penalized statistical methods. The pipeline uses a step-wise approach to transform and personalize template information first to a single-subject template (SST) and then to the individual's time series data. The first stream of information flows from group template to the SST; the second flows from the SST to the individual time-points and provides unbiased, prior-based segmentation and measurement of cortical thickness. MRI-bias correction, consistent longitudinal segmentation, cortical parcellation and cortical thickness estimation are all based on strong use of the subject-specific priors built from initial diffeomorphic mapping between the SST and optimal group template. We evaluate our approach with both test-retest data and with application to a driving biological problem. We use test-retest data to show that this approach produces (a) zero change when the retest data contains the same image content as the test data and (b) produces normally distributed, low variance estimates of thickness change centered at zero when test-retest data is collected near in time to test data. We also show that our approach--when combined with sparse canonical correlation analysis--reveals plausible, significant, annualized decline in cortical thickness and white matter volume when contrasting frontotemporal dementia and normal aging.},
  Institution              = {Dept. of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389 USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Algorithms; Analysis of Variance; Brain Diseases, pathology; Brain, pathology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Longitudinal Studies; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Prognosis; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {20879247},
  Timestamp                = {2014.04.29}
}

@Conference{Avants2009a,
  Title                    = {Multivariate Diffeomorphic Analysis of Longitudinal Increase in White Matter Directionality and Decrease in Cortical Thickness between Ages 14 and 18},
  Author                   = {Avants, B. and Cook, P. A. and Pluta, J. nad Duda, J. T. and Rao, H. and Giannetta, J. and Hurt, H. and Das, S. and Gee, J.},
  Booktitle                = {Human Brain Mapping, 15th Annual Meeting, oral presentation.},
  Year                     = {2009},

  Owner                    = {stnava},
  Timestamp                = {2009.03.24}
}

@Conference{Avants2009b,
  Title                    = {Follow-up on Long Term Effects of Prenatal Cocaine Exposure/Poly-Substance Abuse on the Young Adult Brain},
  Author                   = {Avants, B. and Cook, P. A. and Pluta, J. nad Duda, J. T. and Rao, H. and Giannetta, J. and Hurt, H. and Das, S. and Gee, J.},
  Booktitle                = {Pediatric Academic Society, Annual Meeting},
  Year                     = {2009},

  Owner                    = {stnava},
  Timestamp                = {2009.03.24}
}

@Article{Avants2008,
  Title                    = {Multivariate analysis of structural and diffusion imaging in traumatic brain injury.},
  Author                   = {Brian Avants and Jeffrey T Duda and Junghoon Kim and Hui Zhang and John Pluta and James C Gee and John Whyte},
  Journal                  = {Acad Radiol},
  Year                     = {2008},

  Month                    = {Nov},
  Number                   = {11},
  Pages                    = {1360--1375},
  Volume                   = {15},

  Abstract                 = {RATIONALE AND OBJECTIVES: Diffusion tensor (DT) and T1 structural magnetic resonance images provide unique and complementary tools for quantifying the living brain. We leverage both modalities in a diffeomorphic normalization method that unifies analysis of clinical datasets in a consistent and inherently multivariate (MV) statistical framework. We use this technique to study MV effects of traumatic brain injury (TBI). MATERIALS AND METHODS: We contrast T1 and DT image-based measurements in the thalamus and hippocampus of 12 TBI survivors and nine matched controls normalized to a combined DT and T1 template space. The normalization method uses maps that are topology-preserving and unbiased. Normalization is based on the full tensor of information at each voxel and, simultaneously, the similarity between high-resolution features derived from T1 data. The technique is termed symmetric normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics on the local volume and mean diffusion are assessed with Hotelling's T(2) test with correction for multiple comparisons. RESULTS: TBI significantly (false discovery rate P < .05) reduces volume and increases mean diffusion at coincident locations in the mediodorsal thalamus and anterior hippocampus. CONCLUSIONS: SyNMN reveals evidence that TBI compromises the limbic system. This TBI morphometry study and an additional performance evaluation contrasting SyNMN with other methods suggest that the DT component may aid normalization quality.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.acra.2008.07.007},
  Doi                      = {10.1016/j.acra.2008.07.007},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.},
  Keywords                 = {Adult; Brain; Brain Injuries; Cohort Studies; Diffusion Magnetic Resonance Imaging; Echo-Planar Imaging; Female; Hippocampus; Humans; Image Processing, Computer-Assisted; Male; Middle Aged; Multivariate Analysis; Thalamus},
  Owner                    = {stnava},
  Pii                      = {S1076-6332(08)00395-4},
  Pmid                     = {18995188},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1016/j.acra.2008.07.007}
}

@Article{Avants2008c,
  Title                    = {Multivariate analysis of structural and diffusion imaging in traumatic brain injury.},
  Author                   = {Brian Avants and Jeffrey T Duda and Junghoon Kim and Hui Zhang and John Pluta and James C Gee and John Whyte},
  Journal                  = {Acad Radiol},
  Year                     = {2008},

  Month                    = {Nov},
  Number                   = {11},
  Pages                    = {1360--1375},
  Volume                   = {15},

  Abstract                 = {RATIONALE AND OBJECTIVES: Diffusion tensor (DT) and T1 structural magnetic resonance images provide unique and complementary tools for quantifying the living brain. We leverage both modalities in a diffeomorphic normalization method that unifies analysis of clinical datasets in a consistent and inherently multivariate (MV) statistical framework. We use this technique to study MV effects of traumatic brain injury (TBI). MATERIALS AND METHODS: We contrast T1 and DT image-based measurements in the thalamus and hippocampus of 12 TBI survivors and nine matched controls normalized to a combined DT and T1 template space. The normalization method uses maps that are topology-preserving and unbiased. Normalization is based on the full tensor of information at each voxel and, simultaneously, the similarity between high-resolution features derived from T1 data. The technique is termed symmetric normalization for MV neuroanatomy (SyNMN). Voxel-wise MV statistics on the local volume and mean diffusion are assessed with Hotelling's T(2) test with correction for multiple comparisons. RESULTS: TBI significantly (false discovery rate P < .05) reduces volume and increases mean diffusion at coincident locations in the mediodorsal thalamus and anterior hippocampus. CONCLUSIONS: SyNMN reveals evidence that TBI compromises the limbic system. This TBI morphometry study and an additional performance evaluation contrasting SyNMN with other methods suggest that the DT component may aid normalization quality.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.acra.2008.07.007},
  Doi                      = {10.1016/j.acra.2008.07.007},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.},
  Keywords                 = {Adult; Brain; Brain Injuries; Cohort Studies; Diffusion Magnetic Resonance Imaging; Echo-Planar Imaging; Female; Hippocampus; Humans; Image Processing, Computer-Assisted; Male; Middle Aged; Multivariate Analysis; Thalamus},
  Owner                    = {stnava},
  Pii                      = {S1076-6332(08)00395-4},
  Pmid                     = {18995188},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1016/j.acra.2008.07.007}
}

@Conference{Avants2007c,
  Title                    = {Multivariate normalization with symmetric diffeomorphisms: An integrative approach},
  Author                   = {B. Avants and J. T. Duda and H. Zhang and J. C. Gee},
  Booktitle                = {Medical Image Computing and Computer Aided Intervention},
  Year                     = {2007},
  Pages                    = {359-366},
  Volume                   = {2},

  Owner                    = {stnava},
  Timestamp                = {2007.05.01}
}

@Conference{Avants2007d,
  Title                    = {Symmetric Shape Averaging in the Diffeomorphic Space},
  Author                   = {B. Avants and C. L. Epstein and J. C. Gee},
  Booktitle                = {IEEE Symposium on Biomedical Imaging},
  Year                     = {2007},

  Owner                    = {stnava},
  Timestamp                = {2007.02.23}
}

@Article{Avants2006,
  Title                    = {Geodesic image normalization in the space of diffeomorphisms},
  Author                   = {B. Avants and C. L. Epstein and J. C. Gee},
  Journal                  = {Mathematical Foundations of Computational Anatomy},
  Year                     = {2006},
  Pages                    = {125-133}
}

@InProceedings{Avants2005b,
  Title                    = {Geodesic image interpolation: {P}arameterizing and interpolating spatiotemporal images},
  Author                   = {B. Avants and C. L. Epstein and J. C. Gee},
  Booktitle                = {ICCV Workshop on Variational and Level Set Methods},
  Year                     = {2005},
  Pages                    = {247-258}
}

@Unpublished{Avants2004,
  Title                    = {A method for conformally mapping simply connected domains to the unit disc},
  Author                   = {B. Avants and C. L. Epstein and J. C. Gee},
  Note                     = {in preparation},
  Year                     = {2004}
}

@Article{Avants2004a,
  Title                    = {Geodesic estimation for large deformation anatomical shape and intensity averaging},
  Author                   = {B. Avants and J.C. Gee},
  Journal                  = {Neuroimage},
  Year                     = {2004},
  Pages                    = {S139-150},
  Volume                   = {Suppl. 1}
}

@Article{Avants2004b,
  Title                    = {Geodesic estimation for large deformation anatomical shape and intensity averaging},
  Author                   = {B. Avants and J.C. Gee},
  Journal                  = {Neuroimage},
  Year                     = {2004},
  Pages                    = {S139-150},
  Volume                   = {Suppl. 1}
}

@InProceedings{Avants2003,
  Title                    = {Continuous Curve Matching with Scale-Space Curvature and Extrema-Based Scale Selection},
  Author                   = {B. Avants and J.C. Gee},
  Booktitle                = {Scale-Space Theories in Computer Vision},
  Year                     = {2003},
  Note                     = {L. Griffin editor, Heidelberg:Springer-Verlag, LNCS},
  Pages                    = {798-813}
}

@Article{Avants2003a,
  Title                    = {The shape operator for differential analysis of images.},
  Author                   = {Brian Avants and James Gee},
  Journal                  = {Inf Process Med Imaging},
  Year                     = {2003},

  Month                    = {Jul},
  Pages                    = {101--113},
  Volume                   = {18},

  Abstract                 = {This work provides a new technique for surface oriented volumetric image analysis. The method makes no assumptions about topology, instead constructing a local neighborhood from image information, such as a segmentation or edge map, to define a surface patch. Neighborhood constructions using extrinsic and intrinsic distances are given. This representation allows one to estimate differential properties directly from the image's Gauss map. We develop a novel technique for this purpose which estimates the shape operator and yields both principal directions and curvatures. Only first derivatives need be estimated, making the method numerically stable. We show the use of these measures for multi-scale classification of image structure by the mean and Gaussian curvatures. Finally, we propose to register image volumes by surface curvature. This is particularly useful when geometry is the only variable. To illustrate this, we register binary segmented data by surface curvature, both rigidly and non-rigidly. A novel variant of Demons registration, extensible for use with differentiable similarity metrics, is also applied for deformable curvature-driven registration of medical images.},
  Institution              = {University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Algorithms; Animals; Brain; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Pan troglodytes; Pattern Recognition, Automated; Subtraction Technique},
  Owner                    = {stnava},
  Pmid                     = {15344450},
  Timestamp                = {2008.05.29}
}

@Article{Avants2002,
  Title                    = {Morphometry of Brain Curve Anatomy from Similarity Invariant Parametric Matching Incorporating Global Topology},
  Author                   = {B. Avants and J.C. Gee},
  Journal                  = {ISMRM 10th Scientific Meeting and Exhibition},
  Year                     = {2002}
}

@Article{Avants2004d,
  Title                    = {Validation of Plaster Endocast Morphology through {3D CT} Image Analysis},
  Author                   = {B. Avants and J. Gee and P. T. Schoenemann and R. L. Holloway and J. E. Lewis and J. Monge},
  Journal                  = {American Journal of Physical Anthropology},
  Year                     = {2004},
  Pages                    = {Suppl. 38:56},
  Volume                   = {123}
}

@InProceedings{Avants2002a,
  Title                    = {Soft parametric curve matching in scale space},
  Author                   = {B. Avants and J. C. Gee},
  Booktitle                = {Proc. SPIE Medical Imaging 2002: Image Processing},
  Year                     = {2002},

  Address                  = {Bellingham, WA},
  Editor                   = {M. Fitzpatrick and M. Sonka},
  Organization             = {SPIE},
  Pages                    = {1139-1150}
}

@InCollection{Avants2003b,
  Title                    = {Formulation and evaluation of variational curve matching with prior constraints},
  Author                   = {B. Avants and J. C. Gee},
  Booktitle                = {Biomedical Image Registration},
  Publisher                = {Springer-Verlag},
  Year                     = {2003},

  Address                  = {Heidelberg},
  Editor                   = {J. C. Gee and J. B. A. Maintz and M. W. Vannier},
  Pages                    = {21-30}
}

@Article{Avants2004g,
  Title                    = {Geodesic estimation for large deformation anatomical shape averaging and interpolation.},
  Author                   = {Brian Avants and James C Gee},
  Journal                  = {Neuroimage},
  Year                     = {2004},
  Pages                    = {S139--S150},
  Volume                   = {23 Suppl 1},

  Abstract                 = {The goal of this research is to promote variational methods for anatomical averaging that operate within the space of the underlying image registration problem. This approach is effective when using the large deformation viscous framework, where linear averaging is not valid, or in the elastic case. The theory behind this novel atlas building algorithm is similar to the traditional pairwise registration problem, but with single image forces replaced by average forces. These group forces drive an average transport ordinary differential equation allowing one to estimate the geodesic that moves an image toward the mean shape configuration. This model gives large deformation atlases that are optimal with respect to the shape manifold as defined by the data and the image registration assumptions. We use the techniques in the large deformation context here, but they also pertain to small deformation atlas construction. Furthermore, a natural, inherently inverse consistent image registration is gained for free, as is a tool for constant arc length geodesic shape interpolation. The geodesic atlas creation algorithm is quantitatively compared to the Euclidean anatomical average to elucidate the need for optimized atlases. The procedures generate improved average representations of highly variable anatomy from distinct populations.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2004.07.010},
  Doi                      = {10.1016/j.neuroimage.2004.07.010},
  Institution              = {University of Pennsylvania, Philadelphia, PA 19104, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Algorithms; Animals; Brain; Brain Mapping; Databases, Factual; Humans; Linear Models; Magnetic Resonance Imaging; Models, Anatomic; Models, Statistical; Pan troglodytes; Population},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(04)00375-1},
  Pmid                     = {15501083},
  Timestamp                = {2008.05.29},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2004.07.010}
}

@InProceedings{Avants2002b,
  Title                    = {Robust rotations between anatomical curves},
  Author                   = {B. Avants and J. C. Gee},
  Booktitle                = {IEEE International Symposium on Biomedical Imaging},
  Year                     = {2002},

  Address                  = {Piscataway, NJ},
  Pages                    = {337-340},
  Publisher                = {IEEE Press}
}

@InProceedings{Avants2006a,
  Title                    = {Brain imaging: Brain Imaging: Structural differences between adolescent subjects with gestational cocaine exposure (COC) and controls (CON)},
  Author                   = {B. Avants and J. C. Gee and J. Giannetta and D. Shera and H. Hurt},
  Booktitle                = {Poster Symposium Presentation. San Francisco, CA, April 30, 2006; 3870.6, Pediatr Res.},
  Year                     = {2006}
}

@Article{Avants2007e,
  Title                    = {Symmetric Diffeomorphic Image Registration: Evaluating Automated Labeling of Elderly and Neurodegenerative Cortex},
  Author                   = {B. Avants and M. Grossman and J. C. Gee},
  Journal                  = {Medical Image Analysis},
  Year                     = {2007},
  Note                     = {in press},

  Optpages                 = {in press}
}

@Article{Avants2006b,
  Title                    = {Symmetric Diffeomorphic Image Registration: Evaluating Automated Labeling of Elderly and Neurodegenerative Cortex and Frontal Lobe},
  Author                   = {B. Avants and M. Grossman and J. C. Gee},
  Journal                  = {WBIR},
  Year                     = {2006},
  Pages                    = {50-57}
}

@Article{Avants2005d,
  Title                    = {The correlation of cognitive decline with frontotemporal dementia induced annualized gray matter loss using diffeomorphic morphometry.},
  Author                   = {Brian Avants and Murray Grossman and James C Gee},
  Journal                  = {Alzheimer Dis Assoc Disord},
  Year                     = {2005},
  Pages                    = {S25--S28},
  Volume                   = {19 Suppl 1},

  Abstract                 = {This study uses large deformation medical image registration to analyze, in a disease-specific normalized space, the annual rate of gray matter atrophy caused by frontotemporal dementia (FTD) and its correlation with cognitive decline. The analysis consists of three parts. First, a labeled structural MRI atlas is deformed into the shape of an average FTD brain. Second, annualized FTD-related atrophy of gray matter structures is estimated for each patient in the database. Third, the group-wise annualized atrophy rate caused by FTD is correlated, for each gray matter voxel, with declining performance on cognitive tests. This study gives insight into the relationship between FTD-related progressive cortical atrophy and loss in cognitive function.},
  Institution              = {University of Pennsylvania School of Medicine, Philadelphia, 19104-6389, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Aged; Atrophy; Cerebral Cortex; Cognition Disorders; Disease Progression; Humans; Image Processing, Computer-Assisted; Longitudinal Studies; Magnetic Resonance Imaging; Middle Aged; Pick Disease of the Brain},
  Owner                    = {stnava},
  Pii                      = {00002093-200510001-00006},
  Pmid                     = {16317254},
  Timestamp                = {2008.03.22}
}

@Article{Avants2009,
  Title                    = {Longitudinal cortical atrophy in amyotrophic lateral sclerosis with frontotemporal dementia.},
  Author                   = {Brian Avants and Alea Khan and Leo McCluskey and Lauren Elman and Murray Grossman},
  Journal                  = {Arch Neurol},
  Year                     = {2009},

  Month                    = {Jan},
  Number                   = {1},
  Pages                    = {138--139},
  Volume                   = {66},

  Bdsk-url-1               = {http://dx.doi.org/10.1001/archneurol.2008.542},
  Doi                      = {10.1001/archneurol.2008.542},
  Owner                    = {stnava},
  Pii                      = {66/1/138},
  Pmid                     = {19139315},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1001/archneurol.2008.542}
}

@InCollection{Avants2001,
  Title                    = {Computing match functions for curves in R2 and R3 by refining polyline approximations},
  Author                   = {Avants, B. and Siquiera, M. and Gee, J. C.},
  Booktitle                = {Medical Image Computing and Computer-Assisted Intervention},
  Publisher                = {Springer-Verlag},
  Year                     = {2001},

  Address                  = {Heidelberg},
  Editor                   = {Niessen, W. and Viergever, M.},
  Pages                    = {1178-1179}
}

@Article{Avants1999,
  Title                    = {Measuring the electrical conductivity of the earth},
  Author                   = {B. Avants and D. Soodak and G. Ruppeiner},
  Journal                  = {American Journal of Physics},
  Year                     = {1999},
  Number                   = {7},
  Pages                    = {593-598},
  Volume                   = {67}
}

@Article{Avants2000,
  Title                    = {An adaptive minimal path generation technique for vessel tracking in CTA/CE-MRA volume images},
  Author                   = {B. Avants and J. Williams},
  Journal                  = {Medical Image Computing and Computer Assisted Intervention 2000},
  Year                     = {2000},
  Note                     = {S. Delp and A. DiGioia and B. Jaramaz, eds., Heidelberg:Springer-Verlag, LNCS 1935, {\em {\bf 2004}expanded as a chapter in {\bf Quantitative Vessel Analysis} book available from CRC press}},
  Pages                    = {707-716}
}

@Article{Avants2007g,
  Title                    = {Optimal Template Creation with Symmetric Diffeomorphisms: Evaluation of the Template Effect},
  Author                   = {B. Avants and P. Yushkevich and S. Awate and J. C. Gee and J. Detra and M. Korczykowski},
  Journal                  = {Medical Image Analysis},
  Year                     = {2007},
  Pages                    = {sumbitted},

  Owner                    = {stnava},
  Timestamp                = {2007.10.19}
}

@Article{Avants2009c,
  Title                    = {The optimal template effect in studies of hippocampus in diseased populations},
  Author                   = {B. Avants and P. Yushkevich and J. Pluta and J. C. Gee},
  Journal                  = {Neuroimage},
  Year                     = {2009},
  Pages                    = {in press}
}

@Article{Avants2007h,
  Title                    = {Multivariate normalization with symmetric diffeomorphisms for multivariate studies.},
  Author                   = {B. B. Avants and J. T. Duda and H. Zhang and J. C. Gee},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2007},
  Number                   = {Pt 1},
  Pages                    = {359--366},
  Volume                   = {10},

  Abstract                 = {Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Adult; Algorithms; Artificial Intelligence; Brain; Demyelinating Diseases; DiGeorge Syndrome; Diffusion Magnetic Resonance Imaging; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Multivariate Analysis; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Owner                    = {stnava},
  Pmid                     = {18051079},
  Timestamp                = {2008.02.25}
}

@Article{Avants2008a,
  Title                    = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain.},
  Author                   = {B. B. Avants and C. L. Epstein and M. Grossman and J. C. Gee},
  Journal                  = {Med Image Anal},
  Year                     = {2008},

  Month                    = {Feb},
  Number                   = {1},
  Pages                    = {26--41},
  Volume                   = {12},

  Abstract                 = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
  Bdsk-url-1               = {http://dx.doi.org//j.media.2007.06.004},
  Bdsk-url-2               = {http://dx.doi.org/2007.06.004},
  Doi                      = {/j.media.2007.06.004},
  Institution              = {Department of Radiology, University of Pennsylvania, 3600 Market Street, Philadelphia, PA 19104, United States.},
  Owner                    = {stnava},
  Pii                      = {S1361-8415(07)00060-6},
  Pmid                     = {17659998},
  Timestamp                = {2008.02.25},
  Url                      = {http://dx.doi.org//j.media.2007.06.004}
}

@Article{Avants2008b,
  Title                    = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain.},
  Author                   = {B. B. Avants and C. L. Epstein and M. Grossman and J. C. Gee},
  Journal                  = {Med Image Anal},
  Year                     = {2008},

  Month                    = {Feb},
  Number                   = {1},
  Pages                    = {26--41},
  Volume                   = {12},

  Abstract                 = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
  Bdsk-url-1               = {http://dx.doi.org//j.media.2007.06.004},
  Bdsk-url-2               = {http://dx.doi.org/2007.06.004},
  Doi                      = {/j.media.2007.06.004},
  Institution              = {Department of Radiology, University of Pennsylvania, 3600 Market Street, Philadelphia, PA 19104, United States.},
  Owner                    = {stnava},
  Pii                      = {S1361-8415(07)00060-6},
  Pmid                     = {17659998},
  Timestamp                = {2008.02.25},
  Url                      = {http://dx.doi.org//j.media.2007.06.004}
}

@InProceedings{Avants2006c,
  Title                    = {Analyzing Long Term Effects of Cocaine Exposure on Adolescent Brain Structure with Symmetric Diffeomorphisms},
  Author                   = {B. B. Avants and J. Giannetta and J. C. Gee and H. Hurt and J. Wang},
  Booktitle                = {Mathematical Methods in Biomedical Image Analysis, New York City, NY},
  Year                     = {2006}
}

@Article{Avants2007i,
  Title                    = {Effects of heavy in utero cocaine exposure on adolescent caudate morphology.},
  Author                   = {Brian B Avants and Hallam Hurt and Joan M Giannetta and Charles L Epstein and David M Shera and Hengyi Rao and Jiongjiong Wang and James C Gee},
  Journal                  = {Pediatr Neurol},
  Year                     = {2007},

  Month                    = {Oct},
  Number                   = {4},
  Pages                    = {275--279},
  Volume                   = {37},

  Abstract                 = {We assess the effects of in utero cocaine and polysubstance exposure on the adolescent caudate nucleus through high-resolution magnetic resonance imaging. Cocaine exposure may compromise the developing brain through disruption of neural ontogeny in dopaminergic systems, effects secondary to fetal hypoxemia, or altered cerebrovascular reactivity. Cocaine exposure may also lead to neonatal lesions in the caudate. However, long-term or latent effects of intrauterine cocaine exposure are rarely found. We use T(1)-weighted magnetic resonance imaging to quantify caudate nucleus morphology in matched control and exposed groups. The literature suggests that in utero cocaine exposure consequences in adolescents may be subtle, or masked by other variables. Our comparison focuses on contrasting the control group with high-exposure subjects (mothers who reported 2 median of 117 days of cocaine use during pregnancy; 82\% tested positive for cocaine use at term). We use advanced image registration and segmentation tools to quantify left and right caudate morphology. Our results indicate that the caudate is significantly larger in controls versus subjects (P < 0.0025), implying cocaine exposure-related detriments to the dopaminergic system. The right (P < 0.025) and left (P < 0.035) caudate, studied independently, show the same significant trend. Permutation testing and the false discovery rate were used to assess significance.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.pediatrneurol.2007.06.012},
  Doi                      = {10.1016/j.pediatrneurol.2007.06.012},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Adolescent; Caudate Nucleus; Cocaine; Cohort Studies; Dopamine Uptake Inhibitors; Dose-Response Relationship, Drug; Female; Humans; Magnetic Resonance Imaging; Male; Pregnancy; Prenatal Exposure Delayed Effects},
  Owner                    = {stnava},
  Pii                      = {S0887-8994(07)00328-1},
  Pmid                     = {17903672},
  Timestamp                = {2008.02.25},
  Url                      = {http://dx.doi.org/10.1016/j.pediatrneurol.2007.06.012}
}

@Article{Avants2006d,
  Title                    = {Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex.},
  Author                   = {Brian B Avants and P. Thomas Schoenemann and James C Gee},
  Journal                  = {Med Image Anal},
  Year                     = {2006},

  Month                    = {Jun},
  Number                   = {3},
  Pages                    = {397--412},
  Volume                   = {10},

  Abstract                 = {We develop a novel Lagrangian reference frame diffeomorphic image and landmark registration method. The algorithm uses the fixed Langrangian reference frame to define the map between coordinate systems, but also generates and stores the inverse map from the Eulerian to the Lagrangian frame. Computing both maps allows facile computation of both Eulerian and Langrangian quantities. We apply this algorithm to estimating a putative evolutionary change of coordinates between a population of chimpanzee and human cortices. Inter-species functional homologues fix the map explicitly, where they are known, while image similarities guide the alignment elsewhere. This map allows detailed study of the volumetric change between chimp and human cortex. Instead of basing the inter-species study on a single species atlas, we diffeomorphically connect the mean shape and intensity templates for each group. The human statistics then map diffeomorphically into the space of the chimpanzee cortex providing a comparison between species. The population statistics show a significant doubling of the relative prefrontal lobe size in humans, as compared to chimpanzees.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.media.2005.03.005},
  Doi                      = {10.1016/j.media.2005.03.005},
  Institution              = {Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Algorithms; Animals; Anthropometry; Cerebral Cortex; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Magnetic Resonance Imaging; Organ Size; Pan troglodytes; Reproducibility of Results; Sensitivity and Specificity; Species Specificity; Subtraction Technique},
  Owner                    = {stnava},
  Pii                      = {S1361-8415(05)00041-1},
  Pmid                     = {15948659},
  Timestamp                = {2008.05.29},
  Url                      = {http://dx.doi.org/10.1016/j.media.2005.03.005}
}

@Article{AvantsITK,
  Title                    = {The ITK Image Registration Framework},
  Author                   = {Avants, Brian B. and Tustison, Nicholas J.},
  Journal                  = {Front Neuroinform},
  Year                     = {2014},
  Pages                    = {39},
  Volume                   = {7},

  Abstract                 = {Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.},
  Bdsk-url-1               = {http://dx.doi.org/10.3389/fninf.2013.00039},
  Doi                      = {10.3389/fninf.2013.00039},
  Institution              = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24409140},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.3389/fninf.2013.00039}
}

@Article{Avants2011a,
  Title                    = {A reproducible evaluation of ANTs similarity metric performance in brain image registration.},
  Author                   = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.},
  Journal                  = {Neuroimage},
  Year                     = {2011},

  Month                    = {Feb},
  Number                   = {3},
  Pages                    = {2033--2044},
  Volume                   = {54},

  Abstract                 = {The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669$\pm$0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2010.09.025},
  Doi                      = {10.1016/j.neuroimage.2010.09.025},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA. avants@grasp.cis.upenn.edu},
  Keywords                 = {Algorithms; Brain, anatomy /&/ histology; Databases, Factual; Diagnostic Imaging, methods; Head, anatomy /&/ histology; Humans; Image Processing, Computer-Assisted, methods; Linear Models; Models, Anatomic; Models, Neurological; Population; Reproducibility of Results; Software},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(10)01206-1},
  Pmid                     = {20851191},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2010.09.025}
}

@Article{Avants2014,
  Title                    = {The Insight ToolKit image registration framework.},
  Author                   = {Avants, Brian B. and Tustison, Nicholas J. and Stauffer, Michael and Song, Gang and Wu, Baohua and Gee, James C.},
  Journal                  = {Front Neuroinform},
  Year                     = {2014},
  Pages                    = {44},
  Volume                   = {8},

  Abstract                 = {Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in comparison to previous versions of ITK. ITK(4) supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK(4) reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK(4) contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.},
  Doi                      = {10.3389/fninf.2014.00044},
  Institution              = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24817849},
  Timestamp                = {2014.08.07},
  Url                      = {http://dx.doi.org/10.3389/fninf.2014.00044}
}

@Article{Avants2011,
  Title                    = {An open source multivariate framework for n-tissue segmentation with evaluation on public data.},
  Author                   = {Avants, Brian B. and Tustison, Nicholas J. and Wu, Jue and Cook, Philip A. and Gee, James C.},
  Journal                  = {Neuroinformatics},
  Year                     = {2011},

  Month                    = {Dec},
  Number                   = {4},
  Pages                    = {381--400},
  Volume                   = {9},

  Abstract                 = {We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.},
  Bdsk-url-1               = {http://dx.doi.org/10.1007/s12021-011-9109-y},
  Doi                      = {10.1007/s12021-011-9109-y},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, PA 19104, USA. stnava@gmail.com},
  Keywords                 = {Access to Information; Algorithms; Bayes Theorem; Databases, Factual, standards; Humans; Image Processing, Computer-Assisted, methods; Internet, standards; Magnetic Resonance Imaging, methods; Models, Statistical; Pattern Recognition, Automated, methods; Software, standards},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21373993},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1007/s12021-011-9109-y}
}

@Article{Cook2005,
  Title                    = {An automated approach to connectivity-based partitioning of brain structures.},
  Author                   = {P. A. Cook and H. Zhang and B. B. Avants and P. Yushkevich and D. C. Alexander and J. C. Gee and O. Ciccarelli and A. J. Thompson},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2005},
  Number                   = {Pt 1},
  Pages                    = {164--171},
  Volume                   = {8},

  Abstract                 = {We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.},
  Institution              = {Centre for Medical Image Computing, Department of Computer Science, University College London, UK.},
  Keywords                 = {Algorithms; Artificial Intelligence; Corpus Callosum; Diffusion Magnetic Resonance Imaging; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Nerve Fibers, Myelinated; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity},
  Owner                    = {stnava},
  Pmid                     = {16685842},
  Timestamp                = {2008.03.22}
}

@Conference{Das2007,
  Title                    = {Longitudinal Study of Gray Matter Thickness Using Topologically Consistent Cortical Models},
  Author                   = {S. Das and B. Avants and C. Anderson and M. Grossman},
  Booktitle                = {Human Brain Mapping},
  Year                     = {2007},

  Owner                    = {stnava},
  Timestamp                = {2007.02.23}
}

@InProceedings{Das2007a,
  Title                    = {Measuring Cortical Thickness Using Image Domain Local Surface Models: Application to Longitudinal Study of Atrophy in FTD Spectrum Disorders},
  Author                   = {S. Das and B. Avants and M. Grossman and J. C. Gee},
  Booktitle                = {submitted, Medical Image Computing and Computer Aided Intervention},
  Year                     = {2007}
}

@Conference{Das2007b,
  Title                    = {Measuring Cortical Thickness Using An Image Domain Local Surface Model And Topology Preserving Segmentation},
  Author                   = {S. Das and B. Avants and M. Grossman and J. C. Gee},
  Booktitle                = {MMBIA 2007},
  Year                     = {2007},

  Owner                    = {stnava},
  Timestamp                = {2007.10.19}
}

@Article{Das2009,
  Title                    = {Registration based cortical thickness measurement.},
  Author                   = {Sandhitsu R Das and Brian B Avants and Murray Grossman and James C Gee},
  Journal                  = {Neuroimage},
  Year                     = {2009},

  Month                    = {Apr},
  Number                   = {3},
  Pages                    = {867--879},
  Volume                   = {45},

  Abstract                 = {Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.016},
  Doi                      = {10.1016/j.neuroimage.2008.12.016},
  Institution              = {Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. sudas@seas.upenn.edu},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(08)01278-0},
  Pmid                     = {19150502},
  Timestamp                = {2009.04.02},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.016}
}

@InProceedings{Dubb2002,
  Title                    = {Shape characterization of the corpus callosum in {S}chizophrenia using template deformation},
  Author                   = {A. Dubb and B. Avants and R. Gur and J. C. Gee},
  Booktitle                = {Medical Image Computing and Computer-Assisted Intervention},
  Year                     = {2002},

  Address                  = {Heidelberg},
  Editor                   = {R. Kikinis},
  Pages                    = {381-388},
  Publisher                = {Springer-Verlag}
}

@Article{Dubb2003,
  Title                    = {Characterization of sexual dimorphism in the human corpus callosum.},
  Author                   = {Abraham Dubb and Ruben Gur and Brian Avants and James Gee},
  Journal                  = {Neuroimage},
  Year                     = {2003},

  Month                    = {Sep},
  Number                   = {1},
  Pages                    = {512--519},
  Volume                   = {20},

  Abstract                 = {Despite decades of research, there is still no agreement over the presence of gender-based morphologic differences in the human corpus callosum. We approached the problem using a highly precise computational technique for shape comparison. Starting with a prospectively acquired sample of cranial MRIs of healthy volunteers (age ranges 18-84), the variations of individual callosa are quantified with respect to a reference callosum shape in the form of Jacobian determinant maps derived from the geometric transformations that map the reference callosum into anatomic alignment with the subject callosa. Voxelwise t tests performed over the determinant values demonstrated that females had a larger splenium than males (P < 0.001 uncorrected for multiple comparisons) while males possessed a larger genu (P < 0.001). In addition, pointwise Pearson plots using age as a correlate showed a different pattern of age-related changes in male and female callosa, with female splenia tending to expand more with age, while the male genu tended to contract. Our results demonstrate significant morphologic differences in the corpus callosum between genders and a possible sex difference in the neuro-developmental cycle.},
  Institution              = {Department of Bioengineering, Psychiatry, and Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389, USA. adubb@grasp.cis.upenn.edu},
  Keywords                 = {Adolescent; Adult; Algorithms; Cluster Analysis; Corpus Callosum; Female; Humans; Magnetic Resonance Imaging; Male; Prospective Studies; Schizophrenia; Sex Characteristics},
  Owner                    = {stnava},
  Pii                      = {S1053811903003136},
  Pmid                     = {14527611},
  Timestamp                = {2008.05.29}
}

@InProceedings{duda08miccai,
  Title                    = {A Fiber Tractography Based Examination of Neurodegeneration on Language-Network Neuroanatomy},
  Author                   = {Jeffrey T Duda and Brian B Avants and Jane C Asmuth and Hui Zhang and Murray Grossman and James C Gee},
  Booktitle                = {Workshop on Computational Diffusion MRI, Medical Image Computing and Computer-Assisted Intervention},
  Year                     = {2008},
  Month                    = {Sep},
  Pages                    = {191-198},

  File                     = {full proceedings:http\://www.picsl.upenn.edu/cdmri08/proceedings.pdf:PDF},
  Location                 = {New York, NY},
  Owner                    = {jeff}
}

@InProceedings{duda08cvpr,
  Title                    = {Multivariate Analysis of Thalamo-Cortical Connectivity Loss in {TBI}},
  Author                   = {Jeffrey T Duda and Brian B Avants and Junghoon Kim and Hui Zhang and S Patel and John Whyte and James C Gee},
  Booktitle                = {Computer Vision and Pattern Recognition},
  Year                     = {2008},

  Address                  = {Los Alamitos},
  Month                    = {June},
  Pages                    = {1-8},
  Publisher                = {IEEE Computer Society},

  Bdsk-url-1               = {http://dx.doi.org/10.1109/CVPRW.2008.4562992},
  Doi                      = {10.1109/CVPRW.2008.4562992},
  Location                 = {Anchorage, AK},
  Owner                    = {stnava},
  Timestamp                = {2009.07.02}
}

@Article{Fan2007,
  Title                    = {Multivariate examination of brain abnormality using both structural and functional MRI.},
  Author                   = {Yong Fan and Hengyi Rao and Hallam Hurt and Joan Giannetta and Marc Korczykowski and David Shera and Brian B Avants and James C Gee and Jiongjiong Wang and Dinggang Shen},
  Journal                  = {Neuroimage},
  Year                     = {2007},

  Month                    = {Jul},
  Number                   = {4},
  Pages                    = {1189--1199},
  Volume                   = {36},

  Abstract                 = {A multivariate classification approach has been presented to examine the brain abnormalities, i.e., due to prenatal cocaine exposure, using both structural and functional brain images. First, a regional statistical feature extraction scheme was adopted to capture discriminative features from voxel-wise morphometric and functional representations of brain images, in order to reduce the dimensionality of the features used for classification, as well as to achieve the robustness to registration error and inter-subject variations. Then, this feature extraction method was used in conjunction with a hybrid feature selection method and a nonlinear support vector machine for the classification of brain abnormalities. This brain classification approach has been applied to detecting the brain abnormality associated with prenatal cocaine exposure in adolescents. A promising classification performance was achieved on a data set of 49 subjects (24 normal and 25 prenatally cocaine-exposed teenagers), with a leave-one-out cross-validation. Experimental results demonstrated the efficacy of our method, as well as the importance of incorporating both structural and functional images for brain classification. Moreover, spatial patterns of group difference derived from the constructed classifier were mostly consistent with the results of the conventional statistical analysis method. Therefore, the proposed approach provided not only a multivariate classification method for detecting brain abnormalities, but also an alternative way for group analysis of multimodality images.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2007.04.009},
  Doi                      = {10.1016/j.neuroimage.2007.04.009},
  Institution              = {Department of Radiology, University of Pennsylvania, PA 19104, USA. yong.fan@uphs.upenn.edu},
  Keywords                 = {Adolescent; Algorithms; Artificial Intelligence; Brain; Cocaine; Female; Humans; Image Enhancement; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Multivariate Analysis; Nonlinear Dynamics; Pregnancy; Prenatal Exposure Delayed Effects; Sensitivity and Specificity; Software; Street Drugs},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(07)00327-8},
  Pmid                     = {17512218},
  Timestamp                = {2008.03.22},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2007.04.009}
}

@Article{Fischl2012,
  Title                    = {FreeSurfer.},
  Author                   = {Fischl, Bruce},
  Journal                  = {Neuroimage},
  Year                     = {2012},

  Month                    = {Aug},
  Number                   = {2},
  Pages                    = {774--781},
  Volume                   = {62},
  Abstract                 = {FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.},
  Doi                      = {10.1016/j.neuroimage.2012.01.021},
  Institution              = {Athinoula A Martinos Center, Dept. of Radiology, MGH, Harvard Medical School, MA fischl@nmr.mgh.harvard.edu, USA. fischl@nmr.mgh.harvard.edu},
  Keywords                 = {Algorithms; Brain Mapping, history/methods; Brain, anatomy /&/ histology; History, 20th Century; History, 21st Century; Humans; Image Processing, Computer-Assisted, history/methods; Magnetic Resonance Imaging, history/methods; Software, history},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(12)00038-9},
  Pmid                     = {22248573},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2012.01.021}
}

@InCollection{Gee2005,
  Title                    = {Anatomy-based visualizations of diffusion tensor images of brain white matter},
  Author                   = {Gee, J.C. and Zhang, H. and Dubb, A. and Avants, B. and Yushkevich, P. and Duda, J.T.},
  Booktitle                = {Visualization and Image Processing of Tensor Fields},
  Publisher                = {Springer},
  Year                     = {2005},

  Address                  = {Berlin},
  Editor                   = {Weickert, J. and Hagan, H.}
}

@Article{Gee1993,
  Title                    = {Elastically deforming 3D atlas to match anatomical brain images.},
  Author                   = {Gee, J. C. and Reivich, M. and Bajcsy, R.},
  Journal                  = {J Comput Assist Tomogr},
  Year                     = {1993},
  Number                   = {2},
  Pages                    = {225--236},
  Volume                   = {17},
  Abstract                 = {To evaluate our system for elastically deforming a three-dimensional atlas to match anatomical brain images, six deformed versions of an atlas were generated. The deformed atlases were created by elastically mapping an anatomical brain atlas onto different MR brain image volumes. The mapping matches the edges of the ventricles and the surface of the brain; the resultant deformations are propagated through the atlas volume, deforming the remainder of the structures in the process. The atlas was then elastically matched to its deformed versions. The accuracy of the resultant matches was evaluated by determining the correspondence of 32 cortical and subcortical structures. The system on average matched the centroid of a structure to within 1 mm of its true position and fit a structure to within 11\% of its true volume. The overlap between the matched and true structures, defined by the ratio between the volume of their intersection and the volume of their union, averaged 66\%. When the gray-white interface was included for matching, the mean overlap improved to 78\%; each structure was matched to within 0.6 mm of its true position and fit to within 6\% of its true volume. Preliminary studies were also made to determine the effect of the compliance of the atlas on the resultant match.},
  Institution              = {Department of Computer and Information Science, University of Pennsylvania, Philadelphia 19104.},
  Keywords                 = {Anatomy, Artistic; Brain, anatomy /&/ histology; Caudate Nucleus, anatomy /&/ histology; Cerebral Ventricles, anatomy /&/ histology; Female; Frontal Lobe, anatomy /&/ histology; Humans; Image Enhancement; Image Processing, Computer-Assisted; Magnetic Resonance Imaging, methods; Male; Medical Illustration; Medulla Oblongata, anatomy /&/ histology; Mesencephalon, anatomy /&/ histology; Pons, anatomy /&/ histology; Sensitivity and Specificity; Temporal Lobe, anatomy /&/ histology; Thalamus, anatomy /&/ histology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {8454749},
  Timestamp                = {2014.08.26}
}

@Book{Grenander1993,
  Title                    = {General pattern theory-A mathematical study of regular structures},
  Author                   = {Grenander, Ulf},
  Publisher                = {Clarendon Press},
  Year                     = {1993},
  Owner                    = {stnava},
  Timestamp                = {2014.08.26}
}

@Article{Grossman2008,
  Title                    = {Impaired action knowledge in amyotrophic lateral sclerosis.},
  Author                   = {M. Grossman and C. Anderson and A. Khan and B. Avants and L. Elman and L. McCluskey},
  Journal                  = {Neurology},
  Year                     = {2008},

  Month                    = {Oct},
  Number                   = {18},
  Pages                    = {1396--1401},
  Volume                   = {71},

  Abstract                 = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition affecting the motor system, but recent work also shows more widespread cognitive impairment. This study examined performance on measures requiring knowledge of actions, and related performance to MRI cortical atrophy in ALS. METHODS: A total of 34 patients with ALS performed measures requiring word-description matching and associativity judgments with actions and objects. Voxel-based morphometry was used to relate these measures to cortical atrophy using high resolution structural MRI. RESULTS: Patients with ALS were significantly more impaired on measures requiring knowledge of actions than measures requiring knowledge of objects. Difficulty on measures requiring action knowledge correlated with cortical atrophy in motor cortex, implicating degraded knowledge of action features represented in motor cortex of patients with ALS. Performance on measures requiring object knowledge did not correlate with motor cortex atrophy. Several areas correlated with difficulty for both actions and objects, implicating these brain areas in components of semantic memory that are not dedicated to a specific category of knowledge. CONCLUSION: Patients with amyotrophic lateral sclerosis are impaired on measures involving action knowledge, and this appears to be due to at least two sources of impairment: degradation of knowledge about action features represented in motor cortex and impairment on multicategory cognitive components contributing more generally to semantic memory.},
  Bdsk-url-1               = {http://dx.doi.org/10.1212/01.wnl.0000319701.50168.8c},
  Doi                      = {10.1212/01.wnl.0000319701.50168.8c},
  Institution              = {Department of Neurology-2 Gibson, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104-4283, USA. mgrossma@mail.med.upenn.edu},
  Keywords                 = {Aged; Amyotrophic Lateral Sclerosis; Brain; Case-Control Studies; Cognition Disorder; Fema; Humans; Judgment; Knowledge; Magnetic Resonance Imaging; Male; Middle Aged; Motor Activity; Neuropsychological Tests; Pick Disease of the Brain; le; s},
  Owner                    = {stnava},
  Pii                      = {01.wnl.0000319701.50168.8c},
  Pmid                     = {18784377},
  Timestamp                = {2009.06.30},
  Url                      = {http://dx.doi.org/10.1212/01.wnl.0000319701.50168.8c}
}

@Article{M.Grossman10282008,
  Title                    = {{Impaired action knowledge in amyotrophic lateral sclerosis}},
  Author                   = {Grossman, M. and Anderson, C. and Khan, A. and Avants, B. and Elman, L. and McCluskey, L.},
  Journal                  = {Neurology},
  Year                     = {2008},
  Number                   = {18},
  Pages                    = {1396-1401},
  Volume                   = {71},

  Abstract                 = {Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition affecting the motor system, but recent work also shows more widespread cognitive impairment. This study examined performance on measures requiring knowledge of actions, and related performance to MRI cortical atrophy in ALS. Methods: A total of 34 patients with ALS performed measures requiring word-description matching and associativity judgments with actions and objects. Voxel-based morphometry was used to relate these measures to cortical atrophy using high resolution structural MRI. Results: Patients with ALS were significantly more impaired on measures requiring knowledge of actions than measures requiring knowledge of objects. Difficulty on measures requiring action knowledge correlated with cortical atrophy in motor cortex, implicating degraded knowledge of action features represented in motor cortex of patients with ALS. Performance on measures requiring object knowledge did not correlate with motor cortex atrophy. Several areas correlated with difficulty for both actions and objects, implicating these brain areas in components of semantic memory that are not dedicated to a specific category of knowledge. Conclusion: Patients with amyotrophic lateral sclerosis are impaired on measures involving action knowledge, and this appears to be due to at least two sources of impairment: degradation of knowledge about action features represented in motor cortex and impairment on multicategory cognitive components contributing more generally to semantic memory.},
  Bdsk-url-1               = {http://www.neurology.org/cgi/content/abstract/71/18/1396},
  Bdsk-url-2               = {http://dx.doi.org/10.1212/01.wnl.0000319701.50168.8c},
  Doi                      = {10.1212/01.wnl.0000319701.50168.8c},
  Eprint                   = {http://www.neurology.org/cgi/reprint/71/18/1396.pdf},
  Url                      = {http://www.neurology.org/cgi/content/abstract/71/18/1396}
}

@Article{Hopkins2013,
  Title                    = {Regional and hemispheric variation in cortical thickness in chimpanzees (Pan troglodytes).},
  Author                   = {Hopkins, William D. and Avants, Brian B.},
  Journal                  = {J Neurosci},
  Year                     = {2013},

  Month                    = {Mar},
  Number                   = {12},
  Pages                    = {5241--5248},
  Volume                   = {33},

  Abstract                 = {Recent advances in structural magnetic resonance imaging technology and analysis now allows for accurate in vivo measurement of cortical thickness, an important aspect of cortical organization that has historically only been conducted on postmortem brains. In this study, for the first time, we examined regional and lateralized cortical thickness in a sample of 71 chimpanzees for comparison with previously reported findings in humans. We also measured gray and white matter volumes for each subject. The results indicated that chimpanzees showed significant regional variation in cortical thickness with lower values in primary motor and sensory cortex compared with association cortex. Furthermore, chimpanzees showed significant rightward asymmetries in cortical thickness for a number of regions of interest throughout the cortex and leftward asymmetries in white but not gray matter volume. We also found that total and region-specific cortical thickness was significantly negatively correlated with white matter volume. Thus, chimpanzees with greater white matter volumes had thinner cortical thickness. The collective findings are discussed within the context of previous findings in humans and theories on the evolution of cortical organization and lateralization in primates.},
  Bdsk-url-1               = {http://dx.doi.org/10.1523/JNEUROSCI.2996-12.2013},
  Doi                      = {10.1523/JNEUROSCI.2996-12.2013},
  Institution              = {Division on of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Atlanta, GA 30322, USA. whopkins4@gsu.edu},
  Keywords                 = {Animals; Cerebral Cortex, anatomy /&/ histology; Female; Functional Laterality; Humans; Magnetic Resonance Imaging; Male; Organ Size; Pan troglodytes, anatomy /&/ histology; Parietal Lobe, anatomy /&/ histology; Prefrontal Cortex, anatomy /&/ histology; Temporal Lobe, anatomy /&/ histology},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {33/12/5241},
  Pmid                     = {23516289},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1523/JNEUROSCI.2996-12.2013}
}

@InProceedings{Horn1981,
  Title                    = {Determining optical flow},
  Author                   = {Horn, Berthold K and Schunck, Brian G},
  Booktitle                = {1981 Technical Symposium East},
  Year                     = {1981},
  Organization             = {International Society for Optics and Photonics},
  Pages                    = {319--331},
  Owner                    = {stnava},
  Timestamp                = {2014.08.26},
  Url                      = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1231385}
}

@Article{Kim2008,
  Title                    = {Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study.},
  Author                   = {Junghoon Kim and Brian Avants and Sunil Patel and John Whyte and Branch H Coslett and John Pluta and John A Detre and James C Gee},
  Journal                  = {Neuroimage},
  Year                     = {2008},

  Month                    = {Feb},
  Number                   = {3},
  Pages                    = {1014--1026},
  Volume                   = {39},

  Abstract                 = {Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid- and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume >1.5 cm(3)). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain-behavior relationships in the TBI population.},
  Bdsk-url-1               = {http://dx.doi.org/05},
  Doi                      = {05},
  Institution              = {Moss Rehabilitation Research Institute, Albert Einstein Healthcare Network, Philadelphia, PA, USA.},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(07)00901-9},
  Pmid                     = {17999940},
  Timestamp                = {2008.02.25},
  Url                      = {http://dx.doi.org/05}
}

@Article{Kim2008a,
  Title                    = {Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study.},
  Author                   = {Junghoon Kim and Brian Avants and Sunil Patel and John Whyte and Branch H Coslett and John Pluta and John A Detre and James C Gee},
  Journal                  = {Neuroimage},
  Year                     = {2008},

  Month                    = {Feb},
  Number                   = {3},
  Pages                    = {1014--1026},
  Volume                   = {39},

  Abstract                 = {Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid- and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume >1.5 cm(3)). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain-behavior relationships in the TBI population.},
  Bdsk-url-1               = {http://dx.doi.org/05},
  Doi                      = {05},
  Institution              = {Moss Rehabilitation Research Institute, Albert Einstein Healthcare Network, Philadelphia, PA, USA.},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(07)00901-9},
  Pmid                     = {17999940},
  Timestamp                = {2008.02.25},
  Url                      = {http://dx.doi.org/05}
}

@Article{Klein2009,
  Title                    = {Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.},
  Author                   = {Arno Klein and Jesper Andersson and Babak A Ardekani and John Ashburner and Brian Avants and Ming-Chang Chiang and Gary E Christensen and D. Louis Collins and James Gee and Pierre Hellier and Joo Hyun Song and Mark Jenkinson and Claude Lepage and Daniel Rueckert and Paul Thompson and Tom Vercauteren and Roger P Woods and J. John Mann and Ramin V Parsey},
  Journal                  = {Neuroimage},
  Year                     = {2009},

  Month                    = {Jul},
  Number                   = {3},
  Pages                    = {786--802},
  Volume                   = {46},

  Abstract                 = {All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.037},
  Doi                      = {10.1016/j.neuroimage.2008.12.037},
  Institution              = {New York State Psychiatric Institute, Columbia University, NY, NY 10032, USA. arno@binarybottle.com},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(08)01297-4},
  Pmid                     = {19195496},
  Timestamp                = {2009.05.28},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.037}
}

@Article{Massimo2009,
  Title                    = {Neuroanatomy of apathy and disinhibition in frontotemporal lobar degeneration.},
  Author                   = {Lauren Massimo and Chivon Powers and Peachie Moore and Luisa Vesely and Brian Avants and James Gee and David J Libon and Murray Grossman},
  Journal                  = {Dement Geriatr Cogn Disord},
  Year                     = {2009},
  Number                   = {1},
  Pages                    = {96--104},
  Volume                   = {27},

  Abstract                 = {OBJECTIVE: To investigate the neural basis for the behavioral symptoms of frontotemporal lobar degeneration (FTLD) that cause the greatest caregiver distress. BACKGROUND: FTLD is a progressive neurodegenerative disease associated with behavioral disturbances. Group studies have related these behaviors to volume loss on MRI. METHODS: Forty caregivers of patients with the clinical diagnosis of FTLD completed the Neuropsychiatric Inventory. Twelve neuropsychiatric symptoms and the associated caregiver distress were assessed. Optimized voxel-based morphometry identified significant atrophy in subgroups of FTLD patients with isolated behavioral symptoms corresponding to the most distressing behaviors, and we correlated cortical atrophy directly with these distressing behavioral disorders in an unbiased group analysis. RESULTS: The greatest stressors for caregivers were apathy and disinhibition (p < 0.005 for both contrasts). Partially distinct areas of cortical atrophy were associated with these behaviors in both individual patients with these symptoms and group-wide analyses, including the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in apathetic patients, and the medial orbital frontal cortex in disinhibited patients. CONCLUSIONS: Caregiver stress in families of FTLD patients is due in large part to apathy and disinhibition. The anatomic distribution of cortical loss corresponding to these distressing social behaviors includes partially distinct areas within the frontal lobe.},
  Bdsk-url-1               = {http://dx.doi.org/10.1159/000194658},
  Doi                      = {10.1159/000194658},
  Institution              = {Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, Pa., USA.},
  Owner                    = {stnava},
  Pii                      = {000194658},
  Pmid                     = {19158440},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1159/000194658}
}

@Article{Miller2002,
  Title                    = {On the metrics and euler-lagrange equations of computational anatomy.},
  Author                   = {Miller, Michael I. and Trouve, Alain and Younes, Laurent},
  Journal                  = {Annu Rev Biomed Eng},
  Year                     = {2002},
  Pages                    = {375--405},
  Volume                   = {4},
  Abstract                 = {This paper reviews literature, current concepts and approaches in computational anatomy (CA). The model of CA is a Grenander deformable template, an orbit generated from a template under groups of diffeomorphisms. The metric space of all anatomical images is constructed from the geodesic connecting one anatomical structure to another in the orbit. The variational problems specifying these metrics are reviewed along with their associated Euler-Lagrange equations. The Euler equations of motion derived by Arnold for the geodesics in the group of divergence-free volume-preserving diffeomorphisms of incompressible fluids are generalized for the larger group of diffeomorphisms used in CA with nonconstant Jacobians. Metrics that accommodate photometric variation are described extending the anatomical model to incorporate the construction of neoplasm. Metrics on landmarked shapes are reviewed as well as Joshi's diffeomorphism metrics, Bookstein's thin-plate spline approximate-metrics, and Kendall's affine invariant metrics. We conclude by showing recent experimental results from the Toga & Thompson group in growth, the Van Essen group in macaque and human cortex mapping, and the Csernansky group in hippocampus mapping for neuropsychiatric studies in aging and schizophrenia.},
  Doi                      = {10.1146/annurev.bioeng.4.092101.125733},
  Institution              = {Center for Imaging Science, The Johns Hopkins University, Baltimore, Maryland 21218, USA. mim@cis.jhu.edu},
  Keywords                 = {Algorithms; Animals; Brain, anatomy /&/ histology; Humans; Image Processing, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Macaca; Models, Neurological; Models, Statistical},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {092101.125733},
  Pmid                     = {12117763},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1146/annurev.bioeng.4.092101.125733}
}

@Article{Murphy2011,
  Title                    = {Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.},
  Author                   = {Murphy, Keelin and {van Ginneken}, Bram and Reinhardt, Joseph M. and Kabus, Sven and Ding, Kai and Deng, Xiang and Cao, Kunlin and Du, Kaifang and Christensen, Gary E. and Garcia, Vincent and Vercauteren, Tom and Ayache, Nicholas and Commowick, Olivier and Malandain, Gr{\'{e}}goire and Glocker, Ben and Paragios, Nikos and Navab, Nassir and Gorbunova, Vladlena and Sporring, Jon and {de Bruijne}, Marleen and Han, Xiao and Heinrich, Mattias P. and Schnabel, Julia A. and Jenkinson, Mark and Lorenz, Cristian and Modat, Marc and McClelland, Jamie R. and Ourselin, S{\'{e}}bastien and Muenzing, Sascha E A. and Viergever, Max A. and {De Nigris}, Dante and Collins, D Louis and Arbel, Tal and Peroni, Marta and Li, Rui and Sharp, Gregory C. and Schmidt-Richberg, Alexander and Ehrhardt, Jan and Werner, Ren{\'{e}} and Smeets, Dirk and Loeckx, Dirk and Song, Gang and Tustison, Nicholas and Avants, Brian and Gee, James C. and Staring, Marius and Klein, Stefan and Stoel, Berend C. and Urschler, Martin and Werlberger, Manuel and Vandemeulebroucke, Jef and Rit, Simon and Sarrut, David and Pluim, Josien P W.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2011},

  Month                    = {Nov},
  Number                   = {11},
  Pages                    = {1901--1920},
  Volume                   = {30},

  Abstract                 = {EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.},
  Bdsk-url-1               = {http://dx.doi.org/10.1109/TMI.2011.2158349},
  Doi                      = {10.1109/TMI.2011.2158349},
  Institution              = {Image Sciences Institute, University Medical Center, Utrecht, The Netherlands.},
  Keywords                 = {Algorithms; Animals; Databases, Factual; Lung, radiography; Observer Variation; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted, methods; Radiography, Thoracic, methods; Reference Standards; Reproducibility of Results; Sensitivity and Specificity; Sheep; Software Validation; Thorax; Tomography, X-Ray Computed, methods},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21632295},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1109/TMI.2011.2158349}
}

@Article{Ng2007,
  Title                    = {Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain.},
  Author                   = {Lydia Ng and Sayan D Pathak and Chihchau Kuan and Chris Lau and Hongwei Dong and Andrew Sodt and Chinh Dang and Brian Avants and Paul Yushkevich and James C Gee and David Haynor and Ed Lein and Allan Jones and Mike Hawrylycz},
  Journal                  = {IEEE/ACM Trans Comput Biol Bioinform},
  Year                     = {2007},
  Number                   = {3},
  Pages                    = {382--393},
  Volume                   = {4},

  Abstract                 = {Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (http://www.brain-map.org).},
  Bdsk-url-1               = {http://dx.doi.org/10.1109/tcbb.2007.1035},
  Doi                      = {10.1109/tcbb.2007.1035},
  Institution              = {Allen Institute for Brain Science, Seattle, WA 98103, USA. lydian@alleninstitute.org},
  Keywords                 = {Algorithms; Animals; Brain; Chromosome Mapping; Computational Biology; Gene Expression Profiling; Imaging, Three-Dimensional; In Situ Hybridization, Fluorescence; Male; Mice; Mice, Inbred C57BL; Microscopy, Fluorescence; Nerve Tissue Proteins; Neurosciences},
  Owner                    = {stnava},
  Pmid                     = {17666758},
  Timestamp                = {2008.05.29},
  Url                      = {http://dx.doi.org/10.1109/tcbb.2007.1035}
}

@Article{Pluta2008,
  Title                    = {Expectation matching for incomplete label driven semi-automated hippocampus segmentation in epilepsy},
  Author                   = {J. Pluta and B. Avants and P. Yushkevich and S. Glynn and S. Awate and J. Detre and D. Mechanic and J. C. Gee},
  Journal                  = {Hippocampus},
  Year                     = {2009},
  Pages                    = {accepted},

  Owner                    = {stnava},
  Timestamp                = {2008.10.06}
}

@Article{Pluta2009,
  Title                    = {Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation.},
  Author                   = {John Pluta and Brian B Avants and Simon Glynn and Suyash Awate and James C Gee and John A Detre},
  Journal                  = {Hippocampus},
  Year                     = {2009},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {565--571},
  Volume                   = {19},

  Abstract                 = {We present a robust, high-throughput, semiautomated template-based protocol for segmenting the hippocampus in temporal lobe epilepsy. The semiautomated component of this approach, which minimizes user effort while maximizing the benefit of human input to the algorithm, relies on "incomplete labeling." Incomplete labeling requires the user to quickly and approximately segment a few key regions of the hippocampus through a user-interface. Subsequently, this partial labeling of the hippocampus is combined with image similarity terms to guide volumetric diffeomorphic normalization between an individual brain and an unbiased disease-specific template, with fully labeled hippocampi. We solve this many-to-few and few-to-many matching problem, and gain robustness to inter and intrarater variability and small errors in user labeling, by embedding the template-based normalization within a probabilistic framework that examines both label geometry and appearance data at each label. We evaluate the reliability of this framework with respect to manual labeling and show that it increases minimum performance levels relative to fully automated approaches and provides high inter-rater reliability. Thus, this approach does not require expert neuroanatomical training and is viable for high-throughput studies of both the normal and the highly atrophic hippocampus.},
  Bdsk-url-1               = {http://dx.doi.org/10.1002/hipo.20619},
  Doi                      = {10.1002/hipo.20619},
  Institution              = {d.upenn.edu},
  Owner                    = {stnava},
  Pmid                     = {19437413},
  Timestamp                = {2009.06.30},
  Url                      = {http://dx.doi.org/10.1002/hipo.20619}
}

@InProceedings{Rao2006,
  Title                    = {Altered Resting Brain Function in Prenatally Cocaine-exposed Teenagers: A CASL Perfusion fMRI Study},
  Author                   = {H. Rao and H. Hurt and M. Korczykowski and J. Giannetta and B. B. Avants and J. C. Gee and J. A. Detre and J. Wang},
  Booktitle                = {ISMRM 2006},
  Year                     = {2006}
}

@Article{Rao2007,
  Title                    = {Altered resting cerebral blood flow in adolescents with in utero cocaine exposure revealed by perfusion functional MRI.},
  Author                   = {Hengyi Rao and Jiongjiong Wang and Joan Giannetta and Marc Korczykowski and David Shera and Brian B Avants and James Gee and John A Detre and Hallam Hurt},
  Journal                  = {Pediatrics},
  Year                     = {2007},

  Month                    = {Nov},
  Number                   = {5},
  Pages                    = {e1245--e1254},
  Volume                   = {120},

  Abstract                 = {OBJECTIVES: Animal studies have clearly demonstrated the effects of in utero cocaine exposure on neural ontogeny, especially in dopamine-rich areas of cerebral cortex; however, less is known about how in utero cocaine exposure affects longitudinal neurocognitive development of the human brain. We used continuous arterial spin-labeling perfusion functional MRI to measure the effect of in utero cocaine exposure on resting brain function by comparing resting cerebral blood flow of cocaine-exposed adolescents with non-cocaine-exposed control subjects. PATIENTS AND METHODS: Twenty-four cocaine-exposed adolescents and 25 matched non-cocaine-exposed control subjects underwent structural and perfusion functional MRI during resting states. Direct subtraction, voxel-wise general linear modeling, and region-of-interest analyses were performed on the cerebral blood flow images to compare the resting cerebral blood flow between the 2 groups. RESULTS: Compared with control subjects, cocaine-exposed adolescents showed significantly reduced global cerebral blood flow. The decrease of cerebral blood flow in cocaine-exposed adolescents was observed mainly in posterior and inferior brain regions, including the occipital cortex and thalamus. After adjusting for global cerebral blood flow, however, a significant increase in relative cerebral blood flow in cocaine-exposed adolescents was found in anterior and superior brain regions, including the prefrontal, cingulate, insular, amygdala, and superior parietal cortex. Furthermore, the functional modulations by in utero cocaine exposure on all of these regions except amygdala cannot be accounted for by the variation in brain anatomy. CONCLUSIONS: In utero cocaine exposure may reduce global cerebral blood flow, and this reduction may persist into adolescence. The relative increase of cerebral blood flow in anterior and superior brain regions in cocaine-exposed adolescent participants suggests that compensatory mechanisms for reduced global cerebral blood flow may develop during neural ontogeny. Arterial spin-labeling perfusion MRI may be a valuable tool for investigating the long-term effects of in utero drug exposure.},
  Bdsk-url-1               = {http://dx.doi.org/10.1542/peds.2006-2596},
  Doi                      = {10.1542/peds.2006-2596},
  Institution              = {Department of Radiology and Neurology, Center for Functional Neuroimaging, University of Pennsylvania, Philadelphia, Pennsylvania, USA.},
  Keywords                 = {Adolescent; Age Factors; Blood Circulation Time; Blood Flow Velocity; Cerebrovascular Circulation; Cocaine; Cocaine-Related Disorders; Female; Humans; Magnetic Resonance Imaging; Male; Pregnancy; Prenatal Exposure Delayed Effects; Rest},
  Owner                    = {stnava},
  Pii                      = {120/5/e1245},
  Pmid                     = {17974718},
  Timestamp                = {2008.03.22},
  Url                      = {http://dx.doi.org/10.1542/peds.2006-2596}
}

@Article{Rao2007a,
  Title                    = {Altered Resting Cerebral Blood Flow in Adolescents with In-utero Cocaine Exposure Revealed by Perfusion Functional MRI},
  Author                   = {H. Rao and J. Wang and M. Korczykowski and J. Giannetta and D. Shera and B. Avants and J. Gee and J.A. Detre and H. Hurt},
  Journal                  = {Pediatrics},
  Year                     = {2007},
  Pages                    = {e1245-1254},
  Volume                   = {120},

  Owner                    = {stnava},
  Timestamp                = {2007.02.06}
}

@Article{Rueckert1999,
  Title                    = {Nonrigid registration using free-form deformations: application to breast MR images.},
  Author                   = {Rueckert, D. and Sonoda, L. I. and Hayes, C. and Hill, D. L. and Leach, M. O. and Hawkes, D. J.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {1999},

  Month                    = {Aug},
  Number                   = {8},
  Pages                    = {712--721},
  Volume                   = {18},

  __markedentry            = {[stnava:]},
  Abstract                 = {In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.},
  Doi                      = {10.1109/42.796284},
  Institution              = {Division of Radiological Sciences and Medical Engineering, Guy's, King's, and St. Thomas' School of Medicine, King's College London, Guy's Hospital, London, UK. D.Rueckert@umds.ac.uk},
  Keywords                 = {Breast, anatomy /&/ histology; Female; Humans; Image Processing, Computer-Assisted, methods; Magnetic Resonance Imaging, methods},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {10534053},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1109/42.796284}
}

@Article{Schoenemann2007,
  Title                    = {Validation of plaster endocast morphology through 3D CT image analysis.},
  Author                   = {P.T. Schoenemann and J. Gee and B. Avants and R.L. Holloway and J. Monge and J. Lewis},
  Journal                  = {Am J Phys Anthropol},
  Year                     = {2007},
  Pages                    = {183-92},
  Volume                   = {132},

  Eid                      = {PMID: 17103425},
  Owner                    = {stnava},
  Timestamp                = {2007.02.07}
}

@InProceedings{Schoenemann2004,
  Title                    = {Analysis of chimp-human brain differences via non-rigid deformation of {3D MR} images},
  Author                   = {P. T. Schoenemann and B. B. Avants and J. C. Gee and L. D. Glotzer and M. J. Sheehan},
  Booktitle                = {American Journal of Physical Anthropology},
  Year                     = {2004},
  Pages                    = {174-175},
  Volume                   = {123},

  Optnumber                = {38}
}

@Article{Schoenemann2007a,
  Title                    = {Validation of plaster endocast morphology through 3D CT image analysis.},
  Author                   = {P. Thomas Schoenemann and James Gee and Brian Avants and Ralph L Holloway and Janet Monge and Jason Lewis},
  Journal                  = {Am J Phys Anthropol},
  Year                     = {2007},

  Month                    = {Feb},
  Number                   = {2},
  Pages                    = {183--192},
  Volume                   = {132},

  Abstract                 = {A crucial component of research on brain evolution has been the comparison of fossil endocranial surfaces with modern human and primate endocrania. The latter have generally been obtained by creating endocasts out of rubber latex shells filled with plaster. The extent to which the method of production introduces errors in endocast replicas is unknown. We demonstrate a powerful method of comparing complex shapes in 3-dimensions (3D) that is broadly applicable to a wide range of paleoanthropological questions. Pairs of virtual endocasts (VEs) created from high-resolution CT scans of corresponding latex/plaster endocasts and their associated crania were rigidly registered (aligned) in 3D space for two Homo sapiens and two Pan troglodytes specimens. Distances between each cranial VE and its corresponding latex/plaster VE were then mapped on a voxel-by-voxel basis. The results show that between 79.7\% and 91.0\% of the voxels in the four latex/plaster VEs are within 2 mm of their corresponding cranial VEs surfaces. The average error is relatively small, and variation in the pattern of error across the surfaces appears to be generally random overall. However, inferior areas around the cranial base and the temporal poles were somewhat overestimated in both human and chimpanzee specimens, and the area overlaying Broca's area in humans was somewhat underestimated. This study gives an idea of the size of possible error inherent in latex/plaster endocasts, indicating the level of confidence we can have with studies relying on comparisons between them and, e.g., hominid fossil endocasts.},
  Bdsk-url-1               = {http://dx.doi.org/10.1002/ajpa.20499},
  Doi                      = {10.1002/ajpa.20499},
  Institution              = {Department of Behavioral Sciences, University of Michigan-Dearborn, Dearborn, MI 48128, USA. ptoms@umd.umich.edu},
  Keywords                 = {Animals; Fossils; Humans; Imaging, Three-Dimensional; Paleontology; Pan troglodytes; Skull; Tomography, X-Ray Computed},
  Owner                    = {stnava},
  Pmid                     = {17103425},
  Timestamp                = {2008.05.29},
  Url                      = {http://dx.doi.org/10.1002/ajpa.20499}
}

@Article{Shen2002,
  Title                    = {HAMMER: hierarchical attribute matching mechanism for elastic registration.},
  Author                   = {Shen, Dinggang and Davatzikos, Christos},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2002},

  Month                    = {Nov},
  Number                   = {11},
  Pages                    = {1421--1439},
  Volume                   = {21},
  Abstract                 = {A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.},
  Doi                      = {10.1109/TMI.2002.803111},
  Institution              = {Center for Biomedical Image Computing, Department of Radiology, The Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD 21287, USA. dgshen@rad.upenn.edu},
  Keywords                 = {Aged; Algorithms; Atrophy, diagnosis; Brain, anatomy /&/ histology/pathology; Elasticity; Humans; Image Enhancement, methods; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated; Quality Control; Reproducibility of Results; Sensitivity and Specificity; Stochastic Processes; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {12575879},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1109/TMI.2002.803111}
}

@Article{Simon2008,
  Title                    = {Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome.},
  Author                   = {Tony J Simon and Zhongle Wu and Brian Avants and Hui Zhang and James C Gee and Glenn T Stebbins},
  Journal                  = {Behav Brain Funct},
  Year                     = {2008},
  Pages                    = {25},
  Volume                   = {4},

  Abstract                 = {ABSTRACT: BACKGROUND: Chromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder. METHODS: Whole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention. RESULTS: Analyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation. CONCLUSION: Our results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals.},
  Bdsk-url-1               = {http://dx.doi.org/10.1186/1744-9081-4-25},
  Doi                      = {10.1186/1744-9081-4-25},
  Institution              = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.},
  Owner                    = {stnava},
  Pii                      = {1744-9081-4-25},
  Pmid                     = {18559106},
  Timestamp                = {2008.10.06},
  Url                      = {http://dx.doi.org/10.1186/1744-9081-4-25}
}

@Article{Simon2008a,
  Title                    = {Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome.},
  Author                   = {Tony J Simon and Zhongle Wu and Brian Avants and Hui Zhang and James C Gee and Glenn T Stebbins},
  Journal                  = {Behav Brain Funct},
  Year                     = {2008},
  Pages                    = {25},
  Volume                   = {4},

  Abstract                 = {ABSTRACT: BACKGROUND: Chromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder. METHODS: Whole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention. RESULTS: Analyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation. CONCLUSION: Our results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals.},
  Bdsk-url-1               = {http://dx.doi.org/10.1186/1744-9081-4-25},
  Doi                      = {10.1186/1744-9081-4-25},
  Institution              = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.},
  Owner                    = {stnava},
  Pii                      = {1744-9081-4-25},
  Pmid                     = {18559106},
  Timestamp                = {2008.10.06},
  Url                      = {http://dx.doi.org/10.1186/1744-9081-4-25}
}

@Article{Song2006,
  Title                    = {Integrated graph cuts for brain MRI segmentation.},
  Author                   = {Zhuang Song and Nicholas Tustison and Brian Avants and James C Gee},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2006},
  Number                   = {Pt 2},
  Pages                    = {831--838},
  Volume                   = {9},

  Abstract                 = {Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.},
  Institution              = {Penn Image Computing and Science Lab, University of Pennsylvania, USA. songz@seas.upenn.edu},
  Keywords                 = {Algorithms; Artificial Intelligence; Brain; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity a; nd Specificity},
  Owner                    = {stnava},
  Pmid                     = {17354850},
  Timestamp                = {2008.05.29}
}

@InProceedings{Song2006a,
  Title                    = {Adaptive graph cuts with tissue priors for brain MRI segmentation.},
  Author                   = {Zhuang Song and Nicholas J. Tustison and Brian B. Avants and James C. Gee},
  Booktitle                = {ISBI},
  Year                     = {2006},
  Pages                    = {762-765}
}

@Article{Sun2008,
  Title                    = {Cardiac medial modeling and time-course heart wall thickness analysis.},
  Author                   = {Hui Sun and Brian B Avants and Alejandro F Frangi and Federico Sukno and James C Geel and Paul A Yushkevich},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2008},
  Number                   = {Pt 2},
  Pages                    = {766--773},
  Volume                   = {11},

  Abstract                 = {The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.},
  Keywords                 = {Algorithms; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Cardiovascular; Myocardium; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Owner                    = {stnava},
  Pmid                     = {18982674},
  Timestamp                = {2009.02.14}
}

@Article{Sundaram2005,
  Title                    = {Towards a dynamic model of pulmonary parenchymal deformation: evaluation of methods for temporal reparameterization of lung data.},
  Author                   = {Tessa A Sundaram and Brian B Avants and James C Gee},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2005},
  Number                   = {Pt 2},
  Pages                    = {328--335},
  Volume                   = {8},

  Abstract                 = {We approach the problem of temporal reparameterization of dynamic sequences of lung MR images. In earlier work, we employed capacity-based reparameterization to co-register temporal sequences of 2-D coronal images of the human lungs. Here, we extend that work to the evaluation of a ventilator-acquired 3-D dataset from a normal mouse. Reparameterization according to both deformation and lung volume is evaluated. Both measures provide results that closely approximate normal physiological behavior, as judged from the original data. Our ultimate goal is to be able to characterize normal parenchymal biomechanics over a population of healthy individuals, and to use this statistical model to evaluate lung deformation under various pathological states.},
  Institution              = {University of Pennsylvania, Philadelphia PA 19104, USA.},
  Keywords                 = {Algorithms; Animals; Computer Simulation; Databases, Factual; Elasticity; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Lung; Magnetic Resonance Imaging; Mice; Models, Biological; Reproducibility of Results; Respiratory Mechanics; Sensitivity and Specificity; Subtraction Technique},
  Owner                    = {stnava},
  Pmid                     = {16685976},
  Timestamp                = {2008.05.29}
}

@Article{Thirion1998,
  Title                    = {Image matching as a diffusion process: an analogy with Maxwell's demons.},
  Author                   = {Thirion, J. P.},
  Journal                  = {Med Image Anal},
  Year                     = {1998},

  Month                    = {Sep},
  Number                   = {3},
  Pages                    = {243--260},
  Volume                   = {2},

  __markedentry            = {[stnava:6]},
  Abstract                 = {In this paper, we present the concept of diffusing models to perform image-to-image matching. Having two images to match, the main idea is to consider the objects boundaries in one image as semi-permeable membranes and to let the other image, considered as a deformable grid model, diffuse through these interfaces, by the action of effectors situated within the membranes. We illustrate this concept by an analogy with Maxwell's demons. We show that this concept relates to more traditional ones, based on attraction, with an intermediate step being optical flow techniques. We use the concept of diffusing models to derive three different non-rigid matching algorithms, one using all the intensity levels in the static image, one using only contour points, and a last one operating on already segmented images. Finally, we present results with synthesized deformations and real medical images, with applications to heart motion tracking and three-dimensional inter-patients matching.},
  Institution              = {INRIA, Equipe Epidaure, Sophia-Antipolis, France. jean-philippe.thirion@inria.fr},
  Keywords                 = {Algorithms; Animals; Brain, anatomy /&/ histology; Computer Simulation; Diagnostic Imaging, methods; Dogs; Heart, anatomy /&/ histology; Humans; Image Processing, Computer-Assisted, methods; Models, Theoretical; Thermodynamics},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1361-8415(98)80022-4},
  Pmid                     = {9873902},
  Timestamp                = {2014.08.26}
}

@Article{Thompson2001,
  Title                    = {Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas.},
  Author                   = {Thompson, Paul M. and Mega, Michael S. and Vidal, Christine and Rapoport, Judith L. and Toga, Arthur W.},
  Journal                  = {Inf Process Med Imaging},
  Year                     = {2001},
  Pages                    = {488--501},
  Volume                   = {2082},
  Abstract                 = {The rapid creation of comprehensive brain image databases mandates the development of mathematical algorithms to uncover disease specific patterns of brain structure and function in human populations. We describe our construction of probabilistic atlases that store detailed information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. Specifically, we introduce a mathematical framework based on covariant partial differential equations (PDEs), pull-backs of mappings under harmonic flows, and high-dimensional random tensor fields to encode variations in cortical patterning, asymmetry and tissue distribution in a population-based brain image database (N =94 scans). We use this information to detect disease-specific abnormalities in Alzheimer's disease and schizophrenia, including dynamic changes over time. Illustrative examples are chosen to show how group patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that are not apparent in individual brain images. Finally, we create four-dimensional (4D) maps that store probabilistic information on the dynamics of brain change in development and disease. Digital atlases that generate these maps show considerable promise in identifying general patterns of structural and functional variation in diseased populations, and revealing how these features depend on demographic, genetic, clinical and therapeutic parameters.},
  Doi                      = {10.1007/3-540-45729-1_52},
  Institution              = {Laboratory of Neuro Imaging, Division of Brain Mapping, and Alzheimer's Disease Center, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA thompson@loni.ucla.edu.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21218175},
  Timestamp                = {2014.08.26},
  Url                      = {http://dx.doi.org/10.1007/3-540-45729-1_52}
}

@Article{Tustison2013,
  Title                    = {Explicit B-spline regularization in diffeomorphic image registration.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B.},
  Journal                  = {Front Neuroinform},
  Year                     = {2013},
  Pages                    = {39},
  Volume                   = {7},

  Abstract                 = {Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.},
  Bdsk-url-1               = {http://dx.doi.org/10.3389/fninf.2013.00039},
  Doi                      = {10.3389/fninf.2013.00039},
  Institution              = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24409140},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.3389/fninf.2013.00039}
}

@Article{Tustison2014,
  Title                    = {Logical circularity in voxel-based analysis: normalization strategy may induce statistical bias.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Kim, Junghoon and Whyte, John and Gee, James C. and Stone, James R.},
  Journal                  = {Hum Brain Mapp},
  Year                     = {2014},

  Month                    = {Mar},
  Number                   = {3},
  Pages                    = {745--759},
  Volume                   = {35},

  Abstract                 = {Recent discussions within the neuroimaging community have highlighted the problematic presence of selection bias in experimental design. Although initially centering on the selection of voxels during the course of fMRI studies, we demonstrate how this bias can potentially corrupt voxel-based analyses. For such studies, template-based registration plays a critical role in which a representative template serves as the normalized space for group alignment. A standard approach maps each subject's image to a representative template before performing statistical comparisons between different groups. We analytically demonstrate that in these scenarios the popular sum of squared difference (SSD) intensity metric, implicitly surrogating as a quantification of anatomical alignment, instead explicitly maximizes effect size--an experimental design flaw referred to as "circularity bias." We illustrate how this selection bias varies in strength with the similarity metric used during registration under the hypothesis that while SSD-related metrics, such as Demons, will manifest similar effects, other metrics which are not formulated based on absolute intensity differences will produce less of an effect. Consequently, given the variability in voxel-based analysis outcomes with similarity metric choice, we caution researchers specifically in the use of SSD and SSD-related measures where normalization and statistical analysis involve the same image set. Instead, we advocate a more cautious approach where normalization of the individual subject images to the reference space occurs through corresponding image sets which are independent of statistical testing. Alternatively, one can use similarity terms that are less sensitive to this bias.},
  Bdsk-url-1               = {http://dx.doi.org/10.1002/hbm.22211},
  Doi                      = {10.1002/hbm.22211},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {23151955},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1002/hbm.22211}
}

@Article{Tustison2010,
  Title                    = {N4ITK: improved N3 bias correction.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2010},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {1310--1320},
  Volume                   = {29},

  Abstract                 = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.},
  Bdsk-url-1               = {http://dx.doi.org/10.1109/TMI.2010.2046908},
  Doi                      = {10.1109/TMI.2010.2046908},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19140, USA. ntustison@wustl.edu},
  Keywords                 = {Algorithms; Artifacts; Brain, anatomy /&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {20378467},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1109/TMI.2010.2046908}
}

@Article{Tustison2011,
  Title                    = {Ventilation-based segmentation of the lungs using hyperpolarized (3)He MRI.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Flors, Lucia and Altes, Talissa A. and {de Lange}, Eduard E. and Mugler, 3rd, John P and Gee, James C.},
  Journal                  = {J Magn Reson Imaging},
  Year                     = {2011},

  Month                    = {Oct},
  Number                   = {4},
  Pages                    = {831--841},
  Volume                   = {34},

  Abstract                 = {To develop an automated segmentation method to differentiate the ventilated lung volume on (3) He magnetic resonance imaging (MRI).Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers.There was excellent correlation between the number of ventilation defects tabulated by CP and reader #1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader #2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist #1 (0.743/0.897), radiologist #2 (0.501/0.985), radiologist #3 (0.898/0.848), and the first author (0.600/0.984).We developed and evaluated an automated method for quantifying the ventilated lung volume on (3) He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects.},
  Bdsk-url-1               = {http://dx.doi.org/10.1002/jmri.22738},
  Doi                      = {10.1002/jmri.22738},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA. ntustison@virginia.edu},
  Keywords                 = {Administration, Inhalation; Asthma, diagnosis; Automation; Case-Control Studies; Cystic Fibrosis, diagnosis; Female; Helium, diagnostic use; Humans; Image Processing, Computer-Assisted; Lung, pathology; Magnetic Resonance Imaging, methods; Male; Pulmonary Gas Exchange, physiology; Pulmonary Ventilation, physiology; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21837781},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1002/jmri.22738}
}

@Article{Tustison2009,
  Title                    = {Directly manipulated free-form deformation image registration.},
  Author                   = {Nicholas J Tustison and Brian B Avants and James C Gee},
  Journal                  = {IEEE Trans Image Process},
  Year                     = {2009},

  Month                    = {Mar},
  Number                   = {3},
  Pages                    = {624--635},
  Volume                   = {18},

  Abstract                 = {Previous contributions to both the research and open source software communities detailed a generalization of a fast scalar field fitting technique for cubic B-splines based on the work originally proposed by Lee . One advantage of our proposed generalized B-spline fitting approach is its immediate application to a class of nonrigid registration techniques frequently employed in medical image analysis. Specifically, these registration techniques fall under the rubric of free-form deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object describes the transformation of the image registration solution. Representative of this class of techniques, and often cited within the relevant community, is the formulation of Rueckert who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms, as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained the essential characteristics of Rueckert's original contribution. The contribution which we provide in this paper is two-fold: 1) the observation that the generic FFD framework is intrinsically susceptible to problematic energy topographies and 2) that the standard gradient used in FFD image registration can be modified to a well-understood preconditioned form which substantially improves performance. This is demonstrated with theoretical discussion and comparative evaluation experimentation.},
  Bdsk-url-1               = {http://dx.doi.org/10.1109/TIP.2008.2010072},
  Doi                      = {10.1109/TIP.2008.2010072},
  Owner                    = {stnava},
  Pmid                     = {19171516},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1109/TIP.2008.2010072}
}

@Article{Tustison2011a,
  Title                    = {Topological well-composedness and glamorous glue: a digital gluing algorithm for topologically constrained front propagation.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Siqueira, Marcelo and Gee, James C.},
  Journal                  = {IEEE Trans Image Process},
  Year                     = {2011},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {1756--1761},
  Volume                   = {20},

  Abstract                 = {We propose a new approach to front propagation algorithms based on a topological variant of well-composedness which contrasts with previous methods based on simple point detection. This provides for a theoretical justification, based on the digital Jordan separation theorem, for digitally "gluing" evolved well-composed objects separated by well-composed curves or surfaces. Additionally, our framework can be extended to more relaxed topologically constrained algorithms based on multisimple points. For both methods this framework has the additional benefit of obviating the requirement for both a user-specified connectivity and a topologically-consistent marching cubes/squares algorithm in meshing the resulting segmentation.},
  Bdsk-url-1               = {http://dx.doi.org/10.1109/TIP.2010.2095021},
  Doi                      = {10.1109/TIP.2010.2095021},
  Keywords                 = {Algorithms; Artificial Intelligence; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21118779},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.1109/TIP.2010.2095021}
}

@Article{Tustison2014d,
  Title                    = {Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.},
  Author                   = {Tustison, Nicholas J. and Cook, Philip A. and Klein, Arno and Song, Gang and Das, Sandhitsu R. and Duda, Jeffrey T. and Kandel, Benjamin M. and {van Strien}, Niels and Stone, James R. and Gee, James C. and Avants, Brian B.},
  Journal                  = {Neuroimage},
  Year                     = {2014},

  Month                    = {Oct},
  Pages                    = {166--179},
  Volume                   = {99},
  Abstract                 = {Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan-Killiany-Tourville (DKT) cortical labeling protocol. We found good scan-rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.},
  Doi                      = {10.1016/j.neuroimage.2014.05.044},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(14)00409-1},
  Pmid                     = {24879923},
  Timestamp                = {2014.08.07},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2014.05.044}
}

@Article{Tustison2013a,
  Title                    = {Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences.},
  Author                   = {Tustison, Nicholas J. and Johnson, Hans J. and Rohlfing, Torsten and Klein, Arno and Ghosh, Satrajit S. and Ibanez, Luis and Avants, Brian B.},
  Journal                  = {Front Neurosci},
  Year                     = {2013},
  Pages                    = {162},
  Volume                   = {7},

  Bdsk-url-1               = {http://dx.doi.org/10.3389/fnins.2013.00162},
  Doi                      = {10.3389/fnins.2013.00162},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24058331},
  Timestamp                = {2014.04.29},
  Url                      = {http://dx.doi.org/10.3389/fnins.2013.00162}
}

@Article{Tustison2014c,
  Title                    = {Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR},
  Author                   = {Nicholas J. Tustison and K. L. Shrinhidi and Max Wintermark and Christopher R. Durst and Benjamin M. Kandel and James C. Gee and Murray C. Grossman and Brian B. Avants},
  Journal                  = {Neuroinformatics},
  Year                     = {2014},

  Date-added               = {2014-05-15 23:18:18 +0000},
  Date-modified            = {2014-05-15 23:20:06 +0000}
}

@Article{Wang2011,
  Title                    = {A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation.},
  Author                   = {Wang, Hongzhi and Das, Sandhitsu R. and Suh, Jung Wook and Altinay, Murat and Pluta, John and Craige, Caryne and Avants, Brian and Yushkevich, Paul A. and , Alzheimer's Disease Neuroimaging Initiative},
  Journal                  = {Neuroimage},
  Year                     = {2011},

  Month                    = {Apr},
  Number                   = {3},
  Pages                    = {968--985},
  Volume                   = {55},

  Abstract                 = {We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72\%, 14\%, 29\% and 21\% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.},
  Institution              = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. hongzhiw@mail.med.upenn.edu},
  Keywords                 = {Aged; Algorithms; Alzheimer Disease, pathology; Artificial Intelligence; Atlases as Topic; Brain, anatomy /&/ histology/pathology; Cerebral Cortex, anatomy /&/ histology/pathology; Databases, Factual; Female; Hippocampus, anatomy /&/ histology/pathology; Humans; Image Enhancement, methods; Image Processing, Computer-Assisted, methods; Male; Middle Aged; Software},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21237273},
  Timestamp                = {2014.04.29}
}

@Article{Yushkevich2009,
  Title                    = {A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T.},
  Author                   = {Paul A Yushkevich and Brian B Avants and John Pluta and Sandhitsu Das and David Minkoff and Dawn Mechanic-Hamilton and Simon Glynn and Stephen Pickup and Weixia Liu and James C Gee and Murray Grossman and John A Detre},
  Journal                  = {Neuroimage},
  Year                     = {2009},

  Month                    = {Jan},
  Number                   = {2},
  Pages                    = {385--398},
  Volume                   = {44},

  Abstract                 = {This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated. The atlas is provided as an online resource with the aim of supporting subfield segmentation in emerging hippocampus imaging and image analysis techniques. An example application examining subfield-level hippocampal atrophy in temporal lobe epilepsy demonstrates the application of the atlas to in vivo studies.},
  Bdsk-url-1               = {http://dx.doi.org/10.1016/j.neuroimage.2008.08.042},
  Doi                      = {10.1016/j.neuroimage.2008.08.042},
  Institution              = {Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. pauly2@mail.med.upenn.edu},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(08)00976-2},
  Pmid                     = {18840532},
  Timestamp                = {2009.02.14},
  Url                      = {http://dx.doi.org/10.1016/j.neuroimage.2008.08.042}
}

@Article{Yushkevich2008,
  Title                    = {Shape-based alignment of hippocampal subfields: evaluation in postmortem MRI.},
  Author                   = {Paul A Yushkevich and Brian B Avants and John Pluta and David Minkoff and John A Detre and Murray Grossman and James C Gee},
  Journal                  = {Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv},
  Year                     = {2008},
  Number                   = {Pt 1},
  Pages                    = {510--517},
  Volume                   = {11},

  Abstract                 = {This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.},
  Institution              = {Department of Radiology, University of Pennsylvania, USA.},
  Keywords                 = {of Results; Algorithms; Artificial Intelligence; Cadaver; Computer Simulation; Hippocampus; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Pattern Recogni; Reproducibility; Sensitivity and Specificity; Subtraction Technique; tion, Automated},
  Owner                    = {stnava},
  Pmid                     = {18979785},
  Timestamp                = {2009.02.14}
}

@Article{Zhang2007,
  Title                    = {High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis.},
  Author                   = {Hui Zhang and Brian B Avants and Paul A Yushkevich and John H Woo and Sumei Wang and Leo F McCluskey and Lauren B Elman and Elias R Melhem and James C Gee},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2007},

  Month                    = {Nov},
  Number                   = {11},
  Pages                    = {1585--1597},
  Volume                   = {26},

  Abstract                 = {Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA.},
  Keywords                 = {Adult; Aged; Algorithms; Amyotrophic Lateral Sclerosis; Artificial Intelligence; Brain; Diffusion Magnetic Resonance Imaging; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Male; Middle Aged; Nerve Fibers, Myelinated; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity},
  Owner                    = {stnava},
  Pmid                     = {18041273},
  Timestamp                = {2008.03.22}
}

@InBook{Yoo2004,
  Title                    = {Insight Into Images: Theory for Segmentation, Registration and Image Analysis},
  Chapter                  = {Non-rigid registration},
  Editor                   = {T. Yoo},
  Publisher                = {A. K. Peters Ltd., Natick, MA},
  Year                     = {2004},
  Note                     = {primary author of this chapter}
}