Update DARTEL section of readme

Author: dohmatob

README.rst

@@ -160,8 +160,15 @@ For each subject, the following preprocessing steps are undergone:
 
 DARTEL pipeline
 ---------------
-Motion correction, and coregistration go on as for the standard pipeline. The only difference is the way the subject EPI are warped into MNI space. viz:
-* Group/Inter-subject Normalization is done using the SPM8 [DARTEL](http://www.fil.ion.ucl.ac.uk/spm/software/spm8/SPM8_Release_Notes.pdf) to warp subject brains into MNI space. The idea is to register images by computing a “flow field” which can then be “exponentiated” to generate both forward and backward deformations. Processing begins with the “import” step. This involves taking the parameter files produced by the segmentation (NewSegment), and writing out rigidly transformed versions of the tissue class images, such that they are in as close alignment as possible with the tissue probability maps.   The next step is the registration itself. This involves the simultaneous registration of e.g. GM with GM, WM with WM and 1-(GM+WM) with 1-(GM+WM) (when needed, the 1- (GM+WM) class is generated implicitly, so there is no need to include this class yourself). This procedure begins by creating a mean of all the images, which is used as an initial template. Deformations from this template to each of the individual images are computed, and the template is then re-generated by applying the inverses of the deformations to the images and averaging. This procedure is repeated a number of times.  Finally, warped versions of the images (or other images that are in alignment with them) can be generated.
+Motion correction, and coregistration go on as for the standard pipeline. The only difference between the DARTEL pipeline and the standard one is the way the subject EPI are warped into MNI space.
+
+In the "Dartel pipeline", SPM's [DARTEL](http://www.fil.ion.ucl.ac.uk/spm/software/spm8/SPM8_Release_Notes.pdf) is used to warp subject brains into MNI space.
+
+* The idea is to register images by computing a “flow field” which can then be “exponentiated” to generate both forward and backward deformations. Processing begins with the “import” step. This involves taking the parameter files produced by the segmentation (NewSegment), and writing out rigidly transformed versions of the tissue class images, such that they are in as close alignment as possible with the tissue probability maps.
+
+* The next step is the registration itself. This involves the simultaneous registration of e.g. GM with GM, WM with WM and 1-(GM+WM) with 1-(GM+WM) (when needed, the 1- (GM+WM) class is generated implicitly, so there is no need to include this class yourself). This procedure begins by creating a mean of all the images, which is used as an initial template. Deformations from this template to each of the individual images are computed, and the template is then re-generated by applying the inverses of the deformations to the images and averaging. This procedure is repeated a number of times.  
+
+* Finally, warped versions of the images (or other images that are in alignment with them) can be generated.
 [nipype_preproc_spm_abide.py](https://github.com/neurospin/pypreprocess/blob/master/scripts/abide_preproc.py) is a script which uses this pipeline to preprocess the [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/).
 
 Intra-subject preprocessing in pure Python (with no compiled code, etc.)