Merge pull request #131 from jhlegarreta/AddRefsToREADME

DOC: Add missing references in `README` file

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
+   :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)