Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

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 T 1-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. </p

Deep-learning-based preprocessing for quantitative myocardial perfusion MRI

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 T 1 -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

2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance

Aims Developments in myocardial perfusion cardiovascular magnetic resonance (CMR) allow improvements in spatial resolution and/or myocardial coverage. Whole heart coverage may provide the most accurate assessment of myocardial ischaemic burden, while high spatial resolution is expected to improve detection of subendocardial ischaemia. The objective of this study was to compare myocardial ischaemic burden as depicted by 2D high resolution and 3D whole heart stress myocardial perfusion in patients with coronary artery disease. Methods and results Thirty-eight patients [age 61 +/- 8 (21% female)] underwent 2D high resolution (spatial resolution 1.2 mm(2)) and 3D whole heart (in-plane spatial resolution 2.3 mm(2)) stress CMR at 3-T in randomized order. Myocardial ischaemic burden (%) was visually quantified as perfusion defect at peak stress perfusion subtracted from subendocardial myocardial scar and expressed as a percentage of the myocardium. Median myocardial ischaemic burden was significantly higher with 2D high resolution compared with 3D whole heart [16.1 (2.0-30.6) vs. 13.4 (5.2-23.2), P = 0.004]. There was excellent agreement between myocardial ischaemic burden (intraclass correlation coefficient 0.81; P < 0.0001), with mean ratio difference between 2D high resolution vs. 3D whole heart 1.28 +/- 0.67 (95% limits of agreement -0.03 to 2.59). When using a 10% threshold for a dichotomous result for presence or absence of significant ischaemia, there was moderate agreement between the methods (kappa = 0.58, P < 0.0001). Conclusion 2D high resolution and 3D whole heart myocardial perfusion stress CMR are comparable for detection of ischaemia. 2D high resolution gives higher values for myocardial ischaemic burden compared with 3D whole heart, suggesting that 2D high resolution is more sensitive for detection of ischaemia.ISSN:2047-2404ISSN:2047-241

Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance

Abstract Background Clinical evaluation of stress perfusion cardiovascular magnetic resonance (CMR) is currently based on visual assessment and has shown high diagnostic accuracy in previous clinical trials, when performed by expert readers or core laboratories. However, these results may not be generalizable to clinical practice, particularly when less experienced readers are concerned. Other factors, such as the level of training, the extent of ischemia, and image quality could affect the diagnostic accuracy. Moreover, the role of rest images has not been clarified. The aim of this study was to assess the diagnostic accuracy of visual assessment for operators with different levels of training and the additional value of rest perfusion imaging, and to compare visual assessment and automated quantitative analysis in the assessment of coronary artery disease (CAD). Methods We evaluated 53 patients with known or suspected CAD referred for stress-perfusion CMR. Nine operators (equally divided in 3 levels of competency) blindly reviewed each case twice with a 2-week interval, in a randomised order, with and without rest images. Semi-automated Fermi deconvolution was used for quantitative analysis and estimation of myocardial perfusion reserve as the ratio of stress to rest perfusion estimates. Results Level-3 operators correctly identified significant CAD in 83.6% of the cases. This percentage dropped to 65.7% for Level-2 operators and to 55.7% for Level-1 operators (p < 0.001). Quantitative analysis correctly identified CAD in 86.3% of the cases and was non-inferior to expert readers (p = 0.56). When rest images were available, a significantly higher level of confidence was reported (p = 0.022), but no significant differences in diagnostic accuracy were measured (p = 0.34). Conclusions Our study demonstrates that the level of training is the main determinant of the diagnostic accuracy in the identification of CAD. Level-3 operators performed at levels comparable with the results from clinical trials. Rest images did not significantly improve diagnostic accuracy, but contributed to higher confidence in the results. Automated quantitative analysis performed similarly to level-3 operators. This is of increasing relevance as recent technical advances in image reconstruction and analysis techniques are likely to permit the clinical translation of robust and fully automated quantitative analysis into routine clinical practice

Young athletes: Preventing sudden death by adopting a modern screening approach? A critical review and the opening of a debate

Preventing sudden cardiac death (SCD) in athletes is a primary duty of sports cardiologists. Current recommendations for detecting high-risk cardiovascular conditions (hr-CVCs) are history and physical examination (H&P)-based. We discuss the effectiveness of H&P-based screening versus more-modern and accurate methods. In this position paper, we review current authoritative statements and suggest a novel alternative: screening MRI (s-MRI), supported by evidence from a preliminary population-based study (completed in 2018), and a prospective, controlled study in military recruits (in development).We present: 1. Literature-Based Comparisons (for diagnosing hr-CVCs): Two recent studies using traditional methods to identify hr-CVCs in >3,000 young athletes are compared with our s-MRI-based study of 5,169 adolescents. 2. Critical Review of Previous Results: The reported incidence of SCD in athletes is presently based on retrospective, observational, and incomplete studies. H&P’s screening value seems minimal for structural heart disease, versus echocardiography (which improves diagnosis for high-risk cardiomyopathies) and s-MRI (which also identifies high-risk coronary artery anomalies). Electrocardiography is valuable in screening for potentially high-risk electrophysiological anomalies. 3. Proposed Project: We propose a prospective, controlled study (2 comparable large cohorts: one historical, one prospective) to compare: (1) diagnostic accuracy and resulting mortality-prevention performance of traditional screening methods versus questionnaire/electrocardiography/s-MRI, during 2-month periods of intense, structured exercise (in military recruits, in advanced state of preparation); (2) global costs and cost/efficiency between these two methods. This study should contribute significantly toward a comprehensive understanding of the incidence and causes of exercise-related mortality (including establishing a definition of hr-CVCs) while aiming to reduce mortality