From 473796ee82f9b67455cb6ca895e1d43fec006bc3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jon=20Haitz=20Legarreta=20Gorro=C3=B1o?= Date: Fri, 15 Mar 2024 18:41:18 -0400 Subject: [PATCH] DOC: Add missing references in `README` file Add missing references in `README` file. --- README.rst | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index bb5795e2..8dce225e 100644 --- a/README.rst +++ b/README.rst @@ -11,22 +11,34 @@ diffusion MRI (dMRI) experiments renders exceptionally challenging1 for datasets high-diffusivity (or “high b”) images. These “high b” (b > 1000s/mm2) DWIs enable higher angular resolution, as compared to more traditional diffusion tensor imaging (DTI) schemes. -UNDISTORT1 (Using NonDistorted Images to Simulate a Template Of the Registration Target) +UNDISTORT [1]_ (Using NonDistorted Images to Simulate a Template Of the Registration Target) was the earliest method addressing this issue, by simulating a target DW image without motion or distortion from a DTI (b=1000s/mm2) scan of the same subject. -Later, Andersson and Sotiropoulos2 proposed a similar approach (widely available within the +Later, Andersson and Sotiropoulos [2]_ proposed a similar approach (widely available within the FSL ``eddy`` tool), by predicting the target DW image to be registered from the remainder of the dMRI dataset and modeled with a Gaussian process. Besides the need for less data, ``eddy`` has the advantage of implicitly modeling distortions due to Eddy currents. -More recently, Cieslak et al.3 integrated both approaches in *SHORELine*, by +More recently, Cieslak et al. [3]_ integrated both approaches in *SHORELine*, by (i) setting up a leave-one-out prediction framework as in eddy; and -(ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE4 diffusion model. +(ii) replacing eddy’s general-purpose Gaussian process prediction with the SHORE [4]_ diffusion model. *Eddymotion* is an open implementation of eddy-current and head-motion correction that builds upon the work of ``eddy`` and *SHORELine*, while generalizing these methods to multiple acquisition schemes -(single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY5. +(single-shell, multi-shell, and diffusion spectrum imaging) using diffusion models available with DIPY [5]_. .. image:: docs/_static/eddymotion-flowchart.svg - :alt: The eddymotion flowchart \ No newline at end of file + :alt: The eddymotion flowchart + + +.. [1] S. Ben-Amitay et al., Motion correction and registration of high b-value diffusion weighted images, Magnetic + Resonance in Medicine 67:1694–1702 (2012) +.. [2] J. L. R. Andersson. et al., An integrated approach to correction for off-resonance effects and subject movement + in diffusion MR imaging, NeuroImage 125 (2016) 1063–1078 +.. [3] M. Cieslak et al., QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data. + Nature Methods, 18(7), 775–778 (2021) +.. [4] E. Ozarslan et al., Simple Harmonic Oscillator Based Reconstruction and Estimation for Three-Dimensional Q-Space + MRI. in Proc. Intl. Soc. Mag. Reson. Med. vol. 17 1396 (2009) +.. [5] E. Garyfallidis et al., Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8, 8 + (2014)