Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI. Issue 6 (11th November 2019)

• Record Type: Journal Article
• Authors: Scannell, Cian M.; Veta, Mitko; Villa, Adriana D.M.; Sammut, Eva C.; Lee, Jack; Breeuwer, Marcel; Chiribiri, Amedeo
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 6(2020)
• Page Start: 1689
• Page End: 1696
• Publication Date: 2019-11-11
• Subjects: quantitative myocardial perfusion -- convolutional neural networks -- machine learning -- automated image analysis; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26983
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation. Purpose: To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis. Study Type: Retrospective. Population: In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test). Field Strength/Sequence: 3.0T/2D multislice saturation recovery T1 ‐weighted gradient echo sequence. Assessment: Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL‐based processing were compared to the results obtained with the manually processed images. Statistical Tests: Bland–Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator. Results: The mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland–Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per‐myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90]. Data Conclusion: We showed high accuracy, compared to manual processing, for the DL‐based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing. Level of Evidence: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689–1696.