Efficient Analysis of Myocardial Perfusion MRI with Human-in-the-loop Dynamic Quality Control: Initial Results Using the SCMR Registry

Motivation: Accurate segmentation of free-breathing (FB) myocardial perfusion (MP) MRI is a labor-intensive yet necessary preprocessing step. A quality control (QC) tool for deep learning (DL)-based segmentation of FB MP MRI is lacking. Goal(s): Developing a DL-based dynamic QC (dQC) tool for automatic analysis of MP MRI. Approach: Using the discrepancy between patch-based segmentations, a dQC map is derived and quantified into a dQC metric. The utility of this metric in detecting erroneous segmentations is demonstrated by considering a human-in-the-loop (HiTL) framework. Results: Referral of the dQC-detected timeframes to a HiTL has markedly improved the segmentation results when compared to a random referral approach. Impact: We proposed a dynamic quality control tool for automatic segmentation and analysis of free-breathing myocardial perfusion MRI datasets. Our results show that the proposed approach has markedly improved segmentation accuracy when used within a practical and efficient clinician-in-the-loop setting.