Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI

Purpose: To assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial veloc-ity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.Materials and Methods: In this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years +/- 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.Results: Median peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years +/- 12; 41 men), the Cohen kappa among four cardio-thoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating charac-teristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.Conclusion: The deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.

Automated summary

The purpose of this study was to evaluate the feasibility of a newly developed deep learning synthetic strain (DLSS) algorithm to infer myocardial velocity and detect wall motion abnormalities in patients with ischemic heart disease. The DLSS algorithm was developed using a retrospective dataset of cardiac MRI examinations. The algorithm's performance was assessed using receiver operating characteristic curve analysis. The results showed that the DLSS algorithm had comparable performance in inferring myocardial velocity and detecting wall motion abnormalities in patients with ischemic heart disease.