Diffusion MRI Design Notes

This page is a work in progress.

• Win's Pipeline - In project/brain/pipeline/coro/ and install_linux/bin/brain/

coro_dicom2nifti.py  - Dicom files ==> Nifti
coro_register.py     - Coregister a set of MRI images against a reference template image.
                       Produce registered nifti DWIs for performing dti steps.
                       Also creates registration matricies.
coro_dti.py          - Compute a dti; the CIT pipeline does a better job.

• Legacy Pipeline - In project/brain/pipeline/ and install_linux/bin/brain/

format_conversion/   nifti2mriimage - Registered nifti DWI ==> MRI Image
tensor/mritensorop/  mritensor       -             Mri Image ==> DTI and scalar images
streamtube/tubegen/  tubegen         -                   DTI ==> DTI tractogram

tubefa???
project/brain/brainapp               - Interactively create statistics.

• DTK Pipeline tools - In import/dtk/dtk_v0.5_linux/ and import/dtk/trackvis_v0.5_linux/

DTK - DWI => DTI or QBI
      The CIT pipeline does a better job.
DTK - DTI or QBI ==> a tractogram
TrackVis - Produce statistics from a tractogram or QBI.

• New Pipeline in the Works

NYI:  Simple Tubegen - DTI and scalar images ==> Tracts
Scalar sampling
common/libcurvecollection/ -Tractogram culling 
Boolean ROI selection
Stats
project/brain/roi_select/  - Tract filtering.

• GUI Pipeline Interface

Configuration file (.cfg) + Data ==> Custom Python Script (or Makefile or Sh script)
                                 ==> Run the script ==> Intermediate and result files.
                                 ==> Log output

common/libcurvecollection