This pipeline fits a multi-tensor model to diffusion-weighted imaging (DWI) data using the Multi-Resolution Discrete Search (MRDS) method. The Track Orientation Density Imaging (TODI) technique serves as a model selector, determining the optimal number of tensors to fit at each voxel.
Should you use this pipeline for your research, please cite the following
Dhollander, T., Emsell, L., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P. (2014).
Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography.
NeuroImage, 94, 312-336. https://doi.org/10.1016/j.neuroimage.2013.12.047
Coronado-Leija, R., Ramirez-Manzanares, A., Marroquin, J.L. (2017).
Estimation of individual axon bundle properties by a Multi-Resolution Discrete-Search method.
Medical Image Analysis, 42, 26-43. https://doi.org/10.1016/j.media.2017.06.008
Hernandez-Gutierrez, E., Coronado-Leija, R., Ramirez-Manzanares, A., Barakovic, M., Magon, S., Descoteaux, M. (2023).
Improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging.
Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_4
If you are on Linux, we recommend using the Singularity to run mrds_flow pipeline.
If you have Apptainer (Singularity), build the .sif image with:
singularity build mrds-flow_shuffle.sif docker://scilus/mrds-flow:shuffle.
Then, launch your Nextflow command with:
-with-singularity ABSOLUTE_PATH/mrds-flow_shuffle.sif.
If you are on MacOS or Windows, we recommend using the Docker container to run mrds_flow pipeline.
Pull the image with:
docker pull https://hub.docker.com/r/scilus/mrds-flow.
Launch your Nextflow command with:
-with-docker scilus/mrds-flow:shuffle.
See USAGE or run nextflow run main.nf --help