add: long_description.rst based on fmriprep's

Author: oesteban

long_description.rst

@@ -0,0 +1,31 @@
+Preprocessing of diffusion MRI (dMRI) involves numerous steps to clean and standardize
+the data before fitting a particular model.
+Generally, researchers create ad hoc preprocessing workflows for each dataset,
+building upon a large inventory of available tools.
+The complexity of these workflows has snowballed with rapid advances in
+acquisition and processing.
+dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and
+reproducible preprocessing for whole-brain dMRI data.
+dMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
+virtually any dataset, ensuring high-quality preprocessing without manual intervention.
+dMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing
+workflow, which can help ensure the validity of inference and the interpretability
+of results.
+
+The workflow is based on `Nipype <https://nipype.readthedocs.io>`_ and encompases a large
+set of tools from well-known neuroimaging packages, including
+`FSL <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>`_,
+`ANTs <https://stnava.github.io/ANTs/>`_,
+`FreeSurfer <https://surfer.nmr.mgh.harvard.edu/>`_,
+`AFNI <https://afni.nimh.nih.gov/>`_,
+and `Nilearn <https://nilearn.github.io/>`_.
+This pipeline was designed to provide the best software implementation for each state of
+preprocessing, and will be updated as newer and better neuroimaging software becomes
+available.
+
+dMRIPrep performs basic preprocessing steps (coregistration, normalization, unwarping,
+segmentation, skullstripping etc.) providing outputs that can be
+easily submitted to a variety of tractography algorithms.
+
+[Documentation `dmriprep.org <https://dmriprep.readthedocs.io>`_]
+[Support `neurostars.org <https://neurostars.org/tags/fmriprep>`_]