ACVS

The ACVS pipeline (Automated Central Vein Sign) integrates an automated technique for the detection of the central vein sign in white matter lesions, developed by Dr. Jordan Dworkin. It provides processed T1-weighted, T2-FLAIR, and T2star-magnitude images, as well as subject-level CVS probabilities.

Usage

This pipeline contains two stages: 1) Preprocessing and CVS Probability Calculation: calculates the probability of each participant's probability of having CVS+ lesions, and 2) Consolidation: consolidates all participants' CVS results.

Because MIMoSA, ACVS, and APRL follow similar preprocessing steps, their outputs are stored in the same directories. To avoid overwriting files when running any combination of these pipelines on the same data, each pipeline will create only files that were not generated by a previously run pipeline.

This pipeline can be run with or without a container. For containerized usage, Singularity can be used on a cluster or Docker locally. This pipeline can be run in individual or batch mode, meaning you can specify a certain subject and session or run the pipeline for all subjects in the folder, respectively.

These examples will run the pipeline in batch mode on the cluster. To run individually or locally/with a container, set --mode individual, or -c local/-c singularity/-c docker, respectively. Only Step 1 has the option of individual or batch; Step 2 will always run in batch mode.

Step 1. Preprocessing & CVS Probability Calculation

This step processes raw T1, T2-FLAIR, and T2star-magnitude images to prepare for CVS probability calculation. By default, it runs bias correction, HD-BET skullstripping, registration to FLAIR space, WhiteStripe normalization, MIMoSA, CSF extraction, splitting confluent lesions, registration to EPI space, and CVS score calculation.

Required flags:

-m or --mainpath: path to parent data folder
-t or --t1: T1 sequence name
-f or --flair: FLAIR sequence name
-e or --epi: EPI sequence name
--toolpath: path to pipeline folder

Other flags:

-p or --participant: participant ID (only needed for individual mode)
--ses: session ID (only needed for individual mode)
-n or --n4: run N4 bias correction. Default is TRUE
-s or --skullstripping: run skullstripping. Default is TRUE
--stype: skullstripping method: hdbet, fslbet. Default is hdbet
-r or --registration: run registration. Default is TRUE
-w or --whitestripe: run WhiteStripe normalization. Default is TRUE
--mimosa: run MIMoSA segmentation. Default is TRUE
--threshold: threshold for generating MIMoSA mask. Default is 0.2
--csf: extract CSF mask. Default is TRUE
--step: step of pipeline - estimation, consolidation. Default is estimation
--mode: run pipeline individually or batch. Default is batch
-c or --container: which container to use: singularity, docker, local, cluster. Default is cluster
--sinpath: path to singularity image (only needed if using singularity container - don't need to specify if using takim cluster)
--dockerpath: path to docker image (only needed if using docker container)
-h or --help: show help message

bash /path/to/PennSIVE_neuro_pip/pipelines/cvs/code/bash/cvs.sh -m /path/to/data -t "*T1w*.nii.gz" -f "*FLAIR*.nii.gz" -e "*T2star.nii.gz" --toolpath /path/to/PennSIVE_neuro_pip 

Step 2. Consolidation

This step consolidates the CVS results for all participants and sessions.

Required flags:

-m or --mainpath: path to parent data folder
--step: step of pipeline - estimation, consolidation. Default is estimation. This step is consolidation
--toolpath: path to pipeline folder

Other flags:

-c or --container: which container to use: singularity, docker, local, cluster. Default is cluster
--sinpath: path to singularity image (only needed if using singularity container - don't need to specify if using takim cluster)
--dockerpath: path to docker image (only needed if using docker container)
-h or --help: show help message

bash /path/to/PennSIVE_neuro_pip/pipelines/cvs/code/bash/cvs.sh -m /path/to/data --step consolidation --toolpath /path/to/PennSIVE_neuro_pip