Real-time automated assessment of image quality during MRI scanning for optimal image reconstruction

Motivation: MRI scans are often sensitive to subject motion, impacting image quality. One challenge is that it is difficult to detect and mitigate motion-related issues until after the scan has completed. Goal(s): To create a quantitative method for real-time evaluation of MRI scans, identifying events that may corrupt images and enabling prompt decision-making. Approach: The study used simulated data from the ACDC dataset, training a ResNET18 neural network to predict image quality using SSIM scores. Results: Our method can quickly and accurately assess MRI image quality. This could aid motion event detection. However, validation on actual data is needed. Impact: This study introduces an automated, deep-learning based method for real-time assessment of motion-related image quality for cardiac MRI. This innovation can potentially enhance the reliability and efficiency of MRI scans.