What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Integrating Inline Quality Control at the MRI Scanner: Global and Local Assessment of Motion Artifacts Using Deep Learning

Key Points

The deep-learning framework reliably detects motion artifacts, improving diagnostic accuracy and workflow efficiency.
Identifying motion artifacts in abdominal and brain scans enhances image quality, leading to fewer rescans overall.
Inline quality control delivers immediate feedback on image quality, allowing for rapid corrective measures during MRI procedures.
Global and local assessments of MRI imagery improve patient diagnostics, mitigating risks associated with image degradation.

Abstract

Motivation: MRI is essential for diagnostics, yet motion artifacts from patient movement can degrade image quality, risking misdiagnosis and necessitating rescans. Goal(s): Our goal is to provide inline quality assessment of MRI scans to reduce rescans and improve diagnostic accuracy. Approach: We implemented a deep-learning-based framework that evaluates image quality on a global and local level. The framework generates quality reports that can be displayed on the MR console. Results: The framework reliably identified motion artifacts in abdominal and brain scans, providing practical quality feedback. Impact: Our inline integration assessment for global and local image quality in MRI scans enables reliable detection of motion artifacts. This advancement allows for immediate corrective actions, improving diagnostic accuracy and optimizing imaging workflows.

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