Harnessing QA/QC protocols for diffusion MRI neuroimaging workflows with MRIQC
Year: 2024
Type: preprint
Abstract: Motivation. Reliable neuroimaging pipelines require the implementation of robust QA/QC protocols.Goals. Developing an MRIQC extension for 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.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4404415742