Harnessing QA/QC protocols for diffusion MRI neuroimaging workflows with MRIQC

Motivation: Reliable neuroimaging pipelines require the implementation of robust QA/QC protocols. Goal(s): Developing an extension of MRIQC for the QA/QC of diffusion MRI data. Approach: We build on MRIQC's infrastructure to generate individual visual reports of dMRI images and define new image quality metrics (IQMs). Results: We developed a minimal processing pipeline for whole-brain dMRI data of human adults. The processing pipeline generates individual visual reports for the QA of unprocessed inputs. The pipeline also extracts IQMs to train automated decision-making, following MRIQC's established pattern. Impact: MRIQC is a widely-adopted tool for the QA/QC of unprocessed MRI data. However, support for dMRI was previously lacking. This MRIQC extension will improve QA/QC of dMRI by bringing it to the highest standards and will facilitate the implementation of rigorous protocols in multimodal neuroimaging.