Documentation:
- new data quality assessment documentation page, which documents how the reports generated using --data_diagnosis at the analysis stage can inform quality control at the analysis stage
- improved guidelines for RABIES developers
New parameters:
- --includion_ids/--exclusion_ids: these new parameters allow to specify which list of scan should be included/excluded, at any stage of the pipeline
- --bids_filter: allow to specify which BIDS filters to use to select the functional and anatomical files of interest
- --oblique2card: new option to modify the affine in oblique images so these image don't raise an error at later stages
- --inherit_unbiased_template: this novel option allows providing the path to preprocessing outputs from a previous RABIES run, and use the already-generated unbiased template and register images directly onto it instead of creating a new one
Docker container and testing:
- important re-writing of the Dockerfile. The container is much smaller, using only minimal requirements from ANTs, AFNI and FSL, and constructing conda environment based on exact dependencies
- container built and maintained on Github https://github.com/CoBrALab/RABIES/pkgs/container/rabies instead of docker hub
- testing with error_check_rabies.py is more complete (i.e. now tests almost all parameters across pipeline stages), and can take in custom commands to test. Complete testing is also conducted during container build
- we've attached to this release a pre-built singularity image for version 0.5.1 (the file is 1.8Gb). This image can be downloaded and used directly instead of building the container from scratch using singularity.