Tensor-valued dMRI scripts (b-tensor)

The scripts for multi-encoding multi-shell multi-tissue CSD (memsmt-CSD) are based on [memst]. We recommend reading it to understand the scope of the memsmt-CSD problem.

Preparing data for this tutorial

To run lines below, you need a various volumes, b-vector information and masks. The tutorial data is still in preparation, meanwhile you can use this: `

in_dir=where/you/downloaded/tutorial/data
in_dir=$in_dir/btensor

# For now, the tutorial data only contains the masks.
# Other necessary data can be obtained with:
scil_data_download -v ERROR
cp $HOME/.scilpy/btensor_testdata/* $in_dir/

Tip

You may download the complete bash script to run the whole tutorial in one step ⭳ here.

1. Computing the frf

If you want to do CSD with b-tensor data, you should start by computing the fiber response functions. This script should run fast (less than 5 minutes on a full brain). The data in this tutorial is small, with default parameters, we would get a warning (Could not find at least 100 voxels for the WM mask.), so we’ll set the –min_nvox to 1.

scil_frf_memsmt wm_frf.txt gm_frf.txt csf_frf.txt \
    --in_dwis $in_dir/dwi_linear.nii.gz $in_dir/dwi_planar.nii.gz $in_dir/dwi_spherical.nii.gz \
    --in_bvals $in_dir/linear.bvals $in_dir/planar.bvals $in_dir/spherical.bvals \
    --in_bvecs $in_dir/linear.bvecs $in_dir/planar.bvecs $in_dir/spherical.bvecs \
    --in_bdeltas 1 -0.5 0 --min_nvox 1  --mask $in_dir/mask.nii.gz \
    --mask_wm $in_dir/wm_mask.nii.gz --mask_gm $in_dir/gm_mask.nii.gz \
    --mask_csf $in_dir/csf_mask.nii.gz

Note. Ignore the warning that some b-values are high.

2. Computing the fODF

Then, you should compute the fODFs and volume fractions. The following command will save a fODF file for each tissue and a volume fractions file. This script should run in about 1-2 hours for a full brain.

scil_fodf_memsmt wm_frf.txt gm_frf.txt csf_frf.txt \
    --in_dwis $in_dir/dwi_linear.nii.gz $in_dir/dwi_planar.nii.gz $in_dir/dwi_spherical.nii.gz \
    --in_bvals $in_dir/linear.bvals $in_dir/planar.bvals $in_dir/spherical.bvals \
    --in_bvecs $in_dir/linear.bvecs $in_dir/planar.bvecs $in_dir/spherical.bvecs \
    --in_bdeltas 1 -0.5 0  --processes 8 --mask $in_dir/mask.nii.gz

The resulting files are: csf_fodf.nii.gz gm_fodf.nii.gz wm_fodf.nii.gz., as well as the volume fraction map: vf.nii.gz and vf_rgb.nii.gz.

If you want to do DIVIDE with b-tensor data, you should use the following command. It will save files for the MD, uFA, OP, MK_I, MK_A and MK_T. This script should run in about 1-2 hours for a full brain.

scil_btensor_metrics \
    --in_dwis $in_dir/dwi_linear.nii.gz $in_dir/dwi_planar.nii.gz $in_dir/dwi_spherical.nii.gz \
    --in_bvals $in_dir/linear.bvals $in_dir/planar.bvals $in_dir/spherical.bvals \
    --in_bvecs $in_dir/linear.bvecs $in_dir/planar.bvecs $in_dir/spherical.bvecs \
    --in_bdeltas 1 -0.5 0 --fa $in_dir/fa.nii.gz --processes 8 --mask $in_dir/mask.nii.gz

References

[memst]

Karan et al., Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued diffusion MRI. Medical Image Analysis (2022)