Snakemake workflow to convert a clinical dicom directory into BIDS structure.

• dcm2niix (v1.0.20200427)
• python requirements (defined in workflow/envs/mapping.yaml):
  • dcmstack>=0.7.0
  • dicognito>=0.11.0
  • heudiconv>=0.8.0
  • pandas>=0.24.2
  • pydicom>=1.0.2
  • setuptools>=39.2.0
  • snakemake>=5.23.0

The input directory with dicoms should be setup as follows:

data/
├── dicoms/
│   └── <subject>/
│        ├── <sequence>/<dicom_files.dcm>
│        ├── <sequence>/<dicom_files.dcm>
│        └── <sequence>/<dicom_files.dcm>
└── output/

• data the main directory, which stores input subject directories
• dicoms directory that stores the source DICOM files
  • <subject> is the identifier for the subject in the form sub-001, sub-002 etc.
  • <sequence> is the directory for a specific imaging sequence and can be given any name
• output directory will store all outputs from the pipeline

One main feature of this clinical pipeline is that the final output can be stored around a clinical event. For instance if the clinical event was an operation, which included preop, periop, and postop imaging, then the output directory would contain three session folders:

• ses-pre: for imaging data acquired prior to the clinical event
• ses-peri: for imaging data acquired on the same day as the clinical event (i.e. intraoperative imaging)
• ses-post: for imaging data acquired after the clinical event

To divide the imaging data based on a clinical event, the event date is required for each subject. The date should be defined in a tab seperated text file named clinical_events.tsv, which has the clinical event date defined for all subjects.

Here is an example of what this file should look like:

subject	event_date
sub-001	2014_09_28
sub-002	2018_03_26
sub-003	n/a

If the event date for a subject is unknown or the subject, place n/a under event_date for that subject. In this case, the output BIDS directory will contain only the pre session for that subject (with all the imaging data stored there):

output/bids/sub-P001/
  └── ses-pre/anat/...

If you are running this pipeline locally, edit the config/config.yaml file to include the paths to the following:

Variable	Description
dicom_dir	full path to where the input dicom directory is stored
out_dir	full path to where the pipeline should output the data
clinical_event_file [optional]	path to the clinical_events.tsv file with subject clinical event dates
heuristic	heudiconv template file to sort/name dicoms according to BIDS standard
dcm_config	used to modify the input parameters for dcm2niix
anonymize	whether to anonymize the dicom files prior to storing in tar archives (default = True)

How one medical centre acquires imaging for/around a clinical event will differ from another centre. Additionally, patients may experience multiple clinical events, which increases the complexity of ensuring the imaging studies are stored in the appropriate session directory for each clinical event. Within the dicom2bids pipeline, the user can modify how pre, peri and post sessions are defined. Within the config/config.yaml, the following settings should be used to customize:

Variable	Description
peri [default: 0]	number of days ± around clinical event that will deemed peri
dur_multi_event [default: -30]	in the event of multiple clinical events (i.e. repeat surgery), the max num days, before the subsequent clinical event, that imaging studies will be deemed pre1
override_peri [default: True]	in the event an imaging study is deemed peri, but the heuristic file detects a post clinical event flag, it will be moved to post 2

• ¹ the default value is quantified as 30 days before the subsequent clinical event, any imaging acquisitions during that period will be stored in the pre session
• ² this may occur if the clinical event and post event imaging occur on the same day.

DICOM files will first be sorted based on modality and acquisition date. The default DICOM sorting heuristic should work on any dataset but may need some adjustment. The tar archives will then be converted to nifit format and sorted based on image sequence name. This heurisitc will most likely need to be modified based.

The default sort rule can be found in workflow/scripts/dicom2tar/sort_rules.py and is the function sort_rule_clinical. This sort rule separates the DICOM files based on image type (MRI/CT/fluoro) as well as acquisition date. Initially, image acquisitions occurring on different days are stored in separate Tar archives. The tar archives are then given sequentially numbering based on acquisition date.

The sort rule for HeuDiConv is called a heuristic. Refer to this detailed description of the heuristic and how to write your own. The default heuristic file within the dicom2bids workflow can be found in workflow/scripts/heudiconv/clinical_imaging.py.

1. Install Snakemake using Python:

   python -m pip install snakemake

   For installation details, see the instructions in the Snakemake documentation.

2. Clone a copy of this repository to your system:

   git clone https://github.com/greydongilmore/clinical_dicom2bids_smk.git

3. Install the Python dependencies by opening a terminal, changing to the project directory and running:

   python -m pip install -r requirements.txt

All the following commands should be run in the root of the project directory.

1. Prior to running you can do a dry run:

   snakemake -n

2. To locally run the pipeline, run the following:

   snakemake -j $N

   where $N specifies the number of cores to use.

The repository has the following scheme:

├── README.md
├── workflow
│   ├── rules
│   │   └── dicom2bids.smk
│   ├── envs
│   │   └── mapping.yaml
│   ├── scripts
│   |   ├── dicom2tar               # sorts and stores the dicoms into Tarballs
|   │   |   ├── clinical_helpers.py
|   |   |   ├── DicomSorter.py
|   |   |   ├── main.py
|   │   |   └── sort_rules.py
|   |   ├── heudiconv               # heuristic file for clinical imaging
|   │   |   └── clinical_imaging.py 
|   |   └── post_tar2bids           # refactors heudiconv output into final BIDS structure
|   │       └── clean_sessions.py
|   └── Snakefile
├── config
│   └── config.yaml
└── data                            # contains test input dicoms

The dicom2tar pipeline is modified from the dicom2tar master branch (version date:16/07/2020). The code has been modified to fit the dicom2bids workflow.

First, the dicom2tar pipeline will create tar archives with the acquisition date embedded in the archive name:

output/
  └── tars/
        ├── P185_2017_09_15_20170915_I5U57IAQF6Q0.46F51E3C_MR.tar
        ├── P185_2017_11_10_20171110_NW1NTJVCBOJH.F06BAD6C_MR.tar
        ├── P185_2018_03_26_20180326_76EGBNLGE0PA.06FE3A9D_CT.tar
        ├── P185_2018_03_26_20180326_K6S2I5FI1MB9.92C5EAF3_CT.tar
        └── P185_2018_03_27_20180327_9WK33LJUKNJP.F61DC193_CT.tar

The final dicom2tar output will sort the tar archives based on date and assign sequential sessions numbers, which are embedded in the archive name:

output/
  └── tars/
        ├── P185_001_2017_09_15_20170915_I5U57IAQF6Q0.46F51E3C_MR.tar
        ├── P185_002_2017_11_10_20171110_NW1NTJVCBOJH.F06BAD6C_MR.tar
        ├── P185_003_2018_03_26_20180326_76EGBNLGE0PA.06FE3A9D_CT.tar
        ├── P185_004_2018_03_26_20180326_K6S2I5FI1MB9.92C5EAF3_CT.tar
        └── P185_005_2018_03_27_20180327_9WK33LJUKNJP.F61DC193_CT.tar

The output from the rule tar2bids will be:

output/
  └── bids_tmp/
        └── sub-P185/
              ├── ses-001/anat/...
              ├── ses-002/anat/...
              ├── ses-003/anat/...
              ├── ses-004/anat/...
              └── ses-005/anat/...

The output from the rule cleanSessions will be:

output/
  └── bids/
        └── sub-P185/
              ├── ses-peri/anat/...
              ├── ses-post/anat/...
              └── ses-pre/anat/...