Related Experiment Video
Updated: Nov 25, 2025
Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction
Published on: August 17, 2022
Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
Hui Xue1, Rhodri H Davies1, Louise A E Brown1
1National Heart, Lung and Blood Institute, National Institutes of Health, 10 Center Dr, Bethesda, MD 20892 (H.X., P.K.); Barts Heart Centre, Barts Health NHS Trust, London, England (R.H.D., K.D.K., J.C.M.); Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, England (L.A.E.B., S.P.); and National Amyloidosis Centre, Royal Free Hospital, London, England (T.K., M.F.).
Purpose:
To develop a deep neural network-based computational workflow for inline myocardial perfusion analysis that automatically delineates the myocardium, which improves the clinical workflow and offers a "one-click" solution.
Materials And Methods:
In this retrospective study, consecutive adenosine stress and rest perfusion scans were acquired from three hospitals between October 1, 2018 and February 27, 2019. The training and validation set included 1825 perfusion series from 1034 patients (mean age, 60.6 years ± 14.2 [standard deviation]). The independent test set included 200 scans from 105 patients (mean age, 59.1 years ± 12.5). A convolutional neural network (CNN) model was trained to segment the left ventricular cavity, myocardium, and right ventricle by processing an incoming time series of perfusion images. Model outputs were compared with manual ground truth for accuracy of segmentation and flow measures derived on a global and per-sector basis with t test performed for statistical significance. The trained models were integrated onto MR scanners for effective inference.
Results:
The mean Dice ratio of automatic and manual segmentation was 0.93 ± 0.04. The CNN performed similarly to manual segmentation and flow measures for mean stress myocardial blood flow (MBF; 2.25 mL/min/g ± 0.59 vs 2.24 mL/min/g ± 0.59, P = .94) and mean rest MBF (1.08 mL/min/g ± 0.23 vs 1.07 mL/min/g ± 0.23, P = .83). The per-sector MBF values showed no difference between the CNN and manual assessment (P = .92). A central processing unit-based model inference on the MR scanner took less than 1 second for a typical perfusion scan of three slices.
Conclusion:
The described CNN was capable of cardiac perfusion mapping and integrated an automated inline implementation on the MR scanner, enabling one-click analysis and reporting in a manner comparable to manual assessment. Supplemental material is available for this article. © RSNA, 2020.
