Merge pull request #58 from arokem/roadmap

DOC: Roadmap documentation

Author: oesteban

README.rst

@@ -34,17 +34,12 @@ dMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessin
 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.
+The workflow is based on `Nipype <https://nipype.readthedocs.io>`__ and
+encompases a large set of tools from other neuroimaging package. 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 such as head-motion correction,
+susceptibility-derived distortion correction, eddy current correction, etc.
+providing outputs that can be easily submitted to a variety of diffusion models.

docs/index.rst

@@ -10,3 +10,4 @@ Contents
     usage
     api
     changes
+    roadmap

docs/roadmap.rst

@@ -0,0 +1,70 @@
+.. include:: links.rst
+
+--------------------
+Development road map
+--------------------
+This road-map serves as a guide for developers as well as a way for us to
+communicate to users and other stake-holders aboout the expectations they should
+have about the current functionality of the software and future developments.
+
+Version 0.3 (Targetted for March 1st, 2020)
+-------------------------------------------
+This version should be considered an early alpha of the software, but will
+contain a full pipeline of processing from a raw BIDS dataset to analyzable data.
+
+At this point, the processing pipeline will include the following major steps:
+
+#. Susceptibility distortion correction.
+    Using `SDCFlows <https://github.com/poldracklab/sdcflows>`__
+
+#. Signal drift estimation
+    Leveraging the :math:`b=0` extraction, rescaling and averaging that was merged in `#50 <https://github.com/nipreps/dmriprep/pull/50>`__
+
+Version 0.4 (April 1st, 2020)
+-----------------------------
+#. Head motion correction.
+
+    A SHOREline-based approach, ported from QSIPREP. In cases where the data are
+    "shelled", 3dSHORE will be used as the diffusion model. If the data are
+    single-shell, we will use SFM as the diffusion model.
+
+#. Eddy current correction.
+
+    We will explore the possible adaptations of the HMC based on SHOREline above.
+    In cases where the data are "shelled", 3dSHORE will be
+    used as the diffusion model. If the data are single-shell, we will use SFM
+    as the diffusion model.
+
+#. Framewise-displacement calculation
+
+    We will identify volumes that are outliers in terms of head motion, or other
+    severe artifacts that make them likely candidates for exclusion from further
+    analysis.
+
+Version 0.5 (May 1st, 2020)
+----------------------------
+#. Registration between dMRI and T1w image.
+
+#. Identification of outlier measurements (+ imputation?)
+
+If we get around to doing thesee steps earlier, they can also be included in
+earlier releases.
+
+
+Version 1.0 (Targetted for September 2020)
+------------------------------------------
+After integrating the above steps, we will spend the time leading to a 1.0
+testing the software on various datasets, evaluating and validating the
+resulting derivatives.
+
+
+Long-term plans
+---------------
+In the long run we would like to explore the following processing steps:
+
+- Gibbs ringing (using DIPY's image-based implementation).
+- Denoising (e.g., MP-PCA)
+- Rician bias correction
+- Gradient non-linearity correction
+- B1 inhomogeneity field estimation and :abbr:`INU (intensity non-uniformity) correction`
+- Signal drift correction