Open rkotikalapudi opened 1 year ago
@mhmadAbs @spisakt Aim: We need a dedicated Diffusion Weighted Imaging (DWI) processors (modules), that one can integrate to make a systematic DWI-data processing streamline.
What are we dealing with? At the umbrella level, DW images are a stack of 3D brain volumes (4D), that gives useful information on how different regions are of the brain (gray matter ROIs) are connected, i.e. each ROI is a 'node', and each connection is an 'edge' at the graph network level. And the idea is to input the 4D images, and obtain the graph network (i.e., a matrix with nodes and edges). If you have 20 brain regions, you get a 20 x 20 matrix, and so on.
Task 1. Any image is a signal, that is assumed to be corrupted with noise (i.e., confounded by noise). So, here we implement some noise-correction methods (google search key words: eddy correction in DTI, mrtrix3 dwidenoise, mrtrix3 mrdegibbs, etc).
Steps in the Task 1 (each subsequent step's input is a dependency) Outline = eddy_correct > dwidenoise > degibbs and make a binary mask after running the first three steps.
Few tips
@mhmadAbs @spisakt Task 2 - Structural T1w-processing - Part of the anatomical (anat) preprocessing DWI processing will use the derivatives from this task.
Recap - In Task 1, we performed artifact correction on DWI images (e.g., sub-001.dwi.nii.gz). These correction steps included, 1. eddy correction, 2. denoise, 3. degibbs, and 4. creation of a brain mask from the artifact corrected image.
Why do we need Task 2? Referring to Task 1 (section - What are we dealing with?), we need to obtain ROIs that are gray matter regions (cortical and sub-cortical), which will later be used to build the connectome matrix.
Preprocessing steps:
Few links: FastSurfer: https://github.com/Deep-MI/FastSurfer FreeSurfer: https://www.freesurfer.net/ Atlases: https://www.gin.cnrs.fr/en/tools/aal/
Example dataset to run for the mrtrix3