Abstract #1206

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

Dilek M. Yalcinkaya^1,2, Zhuoan Li^1,3, Khalid Youssef⁴, Bobak Heydari⁵, Rohan Dharmakumar^3,4, Robert Judd⁶, Orlando Simonetti⁷, Subha Raman⁴, and Behzad Sharif^1,3,4

¹Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, United States, ²Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States, ³Biomedical Engineering, Purdue University, West Lafayette, IN, United States, ⁴Krannert Cardiovascular Research Center, IUSM, Indianapolis, IN, United States, ⁵Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, AB, Canada, ⁶Intelerad, Raleigh, NC, United States, ⁷Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, United States

Synopsis

Keywords: Analysis/Processing, Segmentation

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.

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