Automated Motion Artifact Check for MRI (AutoMAC-MRI): An Interpretable Framework for Motion Artifact Detection and Severity Assessment

TL;DR

AutoMAC-MRI is an explainable AI framework that accurately detects and grades motion artifacts in MRI images, improving interpretability and aiding in quality control across diverse MRI contrasts and orientations.

Contribution

It introduces a novel contrastive learning-based method for transparent motion artifact grading in MRI, addressing limitations of existing binary and non-interpretable approaches.

Findings

Affinity scores correlate well with expert labels

AutoMAC-MRI achieves accurate motion severity grading

Framework supports real-time MRI quality control

Abstract

Motion artifacts degrade MRI image quality and increase patient recalls. Existing automated quality assessment methods are largely limited to binary decisions and provide little interpretability. We introduce AutoMAC-MRI, an explainable framework for grading motion artifacts across heterogeneous MR contrasts and orientations. The approach uses supervised contrastive learning to learn a discriminative representation of motion severity. Within this feature space, we compute grade-specific affinity scores that quantify an image's proximity to each motion grade, thereby making grade assignments transparent and interpretable. We evaluate AutoMAC-MRI on more than 5000 expert-annotated brain MRI slices spanning multiple contrasts and views. Experiments assessing affinity scores against expert labels show that the scores align well with expert judgment, supporting their use as an interpretable measure of motion severity. By coupling accurate grade detection with per-grade affinity scoring, AutoMAC-MRI enables inline MRI quality control, with the potential to reduce unnecessary rescans and improve workflow efficiency.