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ABCD NDA Fast Track to BIDS conversion tools

Installation

  1. Install Python 3.8 (or above).

  2. Install Poetry.

    python3 -m pip install --user poetry

  3. Clone this repository.

    git clone https://github.com/nimh-dsst/abcd-fasttrack2bids.git

  4. Change into the repository directory.

    cd abcd-fasttrack2bids

  5. Install the Python dependencies.

    poetry install

Resource Request Guidance for pipeline.py

Memory

Request double the amount of concurrent conversions you do (with --n-convert or --n-all) for Memory (in GB). In other words, if you request --n-all or --n-convert of 10, request 20 GB of Memory for the whole process. This is because the dcm2bids which uses dcm2niix process is somehwat memory-intensive and can use up to 2 GB per concurrent conversion.

Disk Space per participant-session

  • Downloaded TGZs: ~### GB
  • Unpacked DICOMs: ~### GB
  • BIDS Data: ~3 GB
  • Temporary Space: ~15 GB

CPUs/threads

You can control the number of concurrent downloads, unpackings, and conversions you want to run with the --n-download, --n-unpack, and --n-convert arguments. Alternatively, you can set all three to the same thing with --n-all. This allows for separately specifying the allowed concurrency on your own local system. For instance, at NIH we use only 6 concurrent downloads to be resepctful of the filesystem and network bandwidth, but 12 concurrent unpackings and 12 concurrent conversions to speed up the the very parallel processes.

Time to filter, download, unpack, convert, correct, and rsync back

The whole workflow regularly runs in less than 45 minutes for one MRI session, usually less than 30 minutes. But it's better to set a maximum time of 60 minutes for one MRI session, just in case. If you group many at once then expect the performance to vary from that.

Warnings

About "corrupt volume" removals

The first 3D volume (60 slices) in some 4D fMRI timeseries gets removed prior to Dcm2Bids/dcm2niix DICOM to NIfTI conversion when the presence of "Raw Data Storage" instead of "MR Image Storage" is in their first slice's Media Storage SOP Class DICOM field (0002,0002). These 4D volumes will have one less 3D volume than expected and these missing timepoints/frames/repetitions should be accounted for during analysis. Scans affected by this alteration are reported inside the scans.tsv file in the rawdata/ output directory.

If you would like more information, you can read the GitHub issue report originally made to dcm2niix @ rordenlab/dcm2niix#830.

About swarm.sh

When using the NIH HPC systems, you can use the swarm.sh script to run everything using biowulf's swarm command. This script is a simple wrapper that first launches the fasttrack2s3.py script to filter the S3 links, then writes a swarm file able to run the pipeline.py script (to download, unpack, and convert) and bids_corrections.py script (to correct the BIDS dataset). It ends by printing out a swarm command that would run the swarm file with the --devel option enabled (which only prints what it would do and actually does nothing). It is good practice to batch the swarm job with the -b option before removing the --devel option from the swarm command.

Since swarm.sh launches fasttrack2s3.py from the BASH script, you should use swarm.sh in an sinteractive terminal session with a minimum of 8GB memory.

Examples

fasttrack2s3.py

Filter by default all series (except quality assurance series) from the ~/abcd_fastqc01.txt file only including the participant-sessions in ~/sessions.csv, then output the filtered abcd_fastqc01.txt files and S3 links to the ~/abcdfasttrack output directory as both combined and separate files per participant-session (thanks to the -sep option).

cd ~/abcd-fasttrack2bids
poetry run python fasttrack2s3.py -csv ~/sessions.csv -sep ~/abcd_fastqc01.txt ~/abcdfasttrack

pipeline.py

  1. Preserving the LOGS files and BIDS data while using 12 download worker threads, 20 concurrent TGZ unpackings, and 25 MRI sessions going through dcm2bids concurrently. This also uses the dcm2bids_v3_config.json configuration file, the NDA package 1234567, the ~/abcd_fastqc01_all_p-20_s-25_s3links.txt S3 links file, a temporary directory of /scratch/abcd, and outputs at the end to the ~/all_p-20_s-25 directory.

    cd ~/abcd-fasttrack2bids
poetry run python pipeline.py -p 1234567 -s ~/abcd_fastqc01_all_p-20_s-25_s3links.txt -c dcm2bids_v3_config.json -t /scratch/abcd -o ~/all_p-20_s-25 -z LOGS BIDS --n-download 12 --n-unpack 20 --n-convert 25

  2. Download the TGZs and unpack them for the DICOMs while only saving the logs and DICOM files. This uses the NDA package 1234567, the ~/sub-NDARINVANONYMIZED_ses-2YearFollowUpYArm1_s3links.txt S3 links file, the dcm2bids_v3_config.json Dcm2Bids configuration file, and outputs to the ~/all_p-1_s-1 directory. This also runs all steps with 5 concurrent parallel commands.

    cd ~/abcd-fasttrack2bids
poetry run python pipeline.py -p 1234567 -s ~/sub-NDARINVANONYMIZED_ses-2YearFollowUpYArm1_s3links.txt -c dcm2bids_v3_config.json -o ~/all_p-1_s-1 -z LOGS DICOM --n-all 5

bids_corrections.py

Correct the BIDS dataset using the "DCAN Labs corrections" at ~/all_p-20_s-25/rawdata using the temporary directory of /scratch/abcd, logging to ~/all_p-20_s-25/code/logs, and using the the MCR v9.1 (MATLAB R2016b compiler runtime environment) directory at ~/MCR/v91.

cd ~/abcd-fasttrack2bids
poetry run python bids_corrections.py -b ~/all_p-20_s-25/rawdata -t /scratch/abcd -l ~/all_p-20_s-25/code/logs --DCAN ~/MCR/v91

Acknowledgements

Thanks to DCAN-Labs/abcd-dicom2bids for:

  1. Instructions on how to prepare the NDA data packages
  2. Inspiration of the dcm2bids version 3 configuration JSON
  3. General order of operations for the NDA's fast track conversion to BIDS
  4. Most of the options in bids_corrections.py