Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction.

Background: Simultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image reconstruction but result in high noise level. Alternatively, iterative reconstruction techniques based on temporal regularization (ITER) improve image quality but are associated with reduced temporal signal fidelity and long computation time limiting their online use. The aim is to develop an image reconstruction technique for SMS-bSSFP myocardial perfusion imaging combining parallel imaging and image-based denoising using a novel noise map estimation network (NoiseMapNet), which preserves both sharpness and temporal signal profiles and that has low computational cost. Methods: The proposed reconstruction of SMS images consists of a standard temporal parallel imaging reconstruction (TGRAPPA) with motion correction (MOCO) followed by image denoising using NoiseMapNet. NoiseMapNet is a deep learning network based on a 2D Unet architecture and aims to predict a noise map from an input noisy image, which is then subtracted from the noisy image to generate the denoised image. This approach was evaluated in 17 patients who underwent stress perfusion imaging using a SMS-bSSFP sequence. Images were reconstructed with (a) TGRAPPA with MOCO (thereafter referred to as TGRAPPA), (b) iterative reconstruction with integrated motion compensation (ITER), and (c) proposed NoiseMapNet-based reconstruction. Normalized mean squared error (NMSE) with respect to TGRAPPA, myocardial sharpness, image quality, perceived SNR (pSNR), and number of diagnostic segments were evaluated. Results: NMSE of NoiseMapNet was lower than using ITER for both myocardium (0.045 ± 0.021 vs. 0.172 ± 0.041, p < 0.001) and left ventricular blood pool (0.025 ± 0.014 vs. 0.069 ± 0.020, p < 0.001). There were no significant differences between all methods for myocardial sharpness (p = 0.77) and number of diagnostic segments (p = 0.36). ITER led to higher image quality than NoiseMapNet/TGRAPPA (2.7 ± 0.4 vs. 1.8 ± 0.4/1.3 ± 0.6, p < 0.001) and higher pSNR than NoiseMapNet/TGRAPPA (3.0 ± 0.0 vs. 2.0 ± 0.0/1.3 ± 0.6, p < 0.001). Importantly, NoiseMapNet yielded higher pSNR (p < 0.001) and image quality (p < 0.008) than TGRAPPA. Computation time of NoiseMapNet was only 20s for one entire dataset. Conclusion: NoiseMapNet-based reconstruction enables fast SMS image reconstruction for stress myocardial perfusion imaging while preserving sharpness and temporal signal profiles.

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Myocardial Blood Flow by Magnetic Resonance in Patients With Suspected Coronary Stenosis: Comparison to PET and Invasive Physiology.

BACKGROUND: Despite recent guideline recommendations, quantitative perfusion (QP) estimates of myocardial blood flow from cardiac magnetic resonance (CMR) have only been sparsely validated. Furthermore, the additional diagnostic value of utilizing QP in addition to the traditional visual expert interpretation of stress-perfusion CMR remains unknown. The aim was to investigate the correlation between myocardial blood flow measurements estimated by CMR, positron emission tomography, and invasive coronary thermodilution. The second aim is to investigate the diagnostic performance of CMR-QP to identify obstructive coronary artery disease (CAD). METHODS: Prospectively enrolled symptomatic patients with >50% diameter stenosis on computed tomography angiography underwent dual-bolus CMR and positron emission tomography with rest and adenosine-stress myocardial blood flow measurements. Subsequently, an invasive coronary angiography (ICA) with fractional flow reserve and thermodilution-based coronary flow reserve was performed. Obstructive CAD was defined as both anatomically severe (>70% diameter stenosis on quantitative coronary angiography) or hemodynamically obstructive (ICA with fractional flow reserve ≤0.80). RESULTS: About 359 patients completed all investigations. Myocardial blood flow and reserve measurements correlated weakly between estimates from CMR-QP, positron emission tomography, and ICA-coronary flow reserve (r<0.40 for all comparisons). In the diagnosis of anatomically severe CAD, the interpretation of CMR-QP by an expert reader improved the sensitivity in comparison to visual analysis alone (82% versus 88% [P=0.03]) without compromising specificity (77% versus 74% [P=0.28]). In the diagnosis of hemodynamically obstructive CAD, the accuracy was only moderate for a visual expert read and remained unchanged when additional CMR-QP measurements were interpreted. CONCLUSIONS: CMR-QP correlates weakly to myocardial blood flow measurements by other modalities but improves diagnosis of anatomically severe CAD. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03481712.

Impact of Anatomical and Viability-Guided Completeness of Revascularization on Clinical Outcomes in Ischemic Cardiomyopathy.

BACKGROUND: Complete revascularization of coronary artery disease has been linked to improved outcomes in patients with preserved left ventricular (LV) function. OBJECTIVES: This study sought to identify the impact of complete revascularization in patients with severe LV dysfunction. METHODS: Patients enrolled in the REVIVED-BCIS2 (Revascularization for Ischemic Ventricular Dysfunction) trial were eligible if baseline/procedural angiograms and viability studies were available for analysis by independent core laboratories. Anatomical and viability-guided completeness of revascularization were measured by the coronary and myocardial revascularization indices (RIcoro and RImyo), respectively, where RIcoro = (change in British Cardiovascular Intervention Society Jeopardy score [BCIS-JS]) / (baseline BCIS-JS) and RImyo= (number of revascularized viable segments) / (number of viable segments supplied by diseased vessels). The percutaneous coronary intervention (PCI) group was classified as having complete or incomplete revascularization by median RIcoro and RImyo. The primary outcome was death or hospitalization for heart failure. RESULTS: Of 700 randomized patients, 670 were included. The baseline BCIS-JS and SYNTAX (Synergy Between PCI With Taxus and Cardiac Surgery) scores were 8 (Q1-Q3: 6-10) and 22 (Q1-Q3: 15-29), respectively. In those patients assigned to PCI, median RIcoro and RImyo values were 67% and 85%, respectively. Compared with the group assigned to optimal medical therapy alone, there was no difference in the likelihood of the primary outcome in those patients receiving complete anatomical or viability-guided revascularization (HR: 0.90; 95% CI: 0.62-1.32; and HR: 0.95; 95% CI: 0.66-1.35, respectively). A sensitivity analysis by residual SYNTAX score showed no association with outcome. CONCLUSIONS: In patients with severe LV dysfunction, neither complete anatomical nor viability-guided revascularization was associated with improved event-free survival compared with incomplete revascularization or treatment with medical therapy alone. (Revascularization for Ischemic Ventricular Dysfunction) [REVIVED-BCIS2]; NCT01920048).

Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records.

Background: Prediction of clinical outcomes in coronary artery disease (CAD) has been conventionally achieved using clinical risk factors. The relationship between imaging features and outcome is still not well understood. This study aims to use artificial intelligence to link image features with mortality outcome. Methods: A retrospective study was performed on patients who had stress perfusion cardiac magnetic resonance (SP-CMR) between 2011 and 2021. The endpoint was all-cause mortality. Convolutional neural network (CNN) was used to extract features from stress perfusion images, and multilayer perceptron (MLP) to extract features from electronic health records (EHRs), both networks were concatenated in a hybrid neural network (HNN) to predict study endpoint. Image CNN was trained to predict study endpoint directly from images. HNN and image CNN were compared with a linear clinical model using area under the curve (AUC), F1 scores, and McNemar's test. Results: Total of 1,286 cases were identified, with 201 death events (16%). The clinical model had good performance (AUC =80%, F1 score =37%). Best Image CNN model showed AUC =72% and F1 score =38%. HNN outperformed the other two models (AUC =82%, F1 score =43%). McNemar's test showed statistical difference between image CNN and both clinical model (P<0.01) and HNN (P<0.01). There was no significant difference between HNN and clinical model (P=0.15). Conclusions: Death in patients with suspected or known CAD can be predicted directly from stress perfusion images without clinical knowledge. Prediction can be improved by HNN that combines clinical and SP-CMR images.

Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.

OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardium contrast. In practice, both DB and WB contrasts may have clinical utility, but acquiring both has the drawback of additional acquisition time. The aim of this study was to develop and evaluate a deep learning method to generate synthetic WB-LGE images from DB-LGE, allowing the assessment of both contrasts without additional scan time. MATERIALS AND METHODS: DB-LGE and WB-LGE data from 215 patients were used to train 2 types of unpaired image-to-image translation deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation, with 5 different loss function hyperparameter settings each. Initially, the best hyperparameter setting was determined for each model type based on the Fréchet inception distance and the visual assessment of expert readers. Then, the CycleGAN and contrastive unpaired translation models with the optimal hyperparameters were directly compared. Finally, with the best model chosen, the quantification of scar based on the synthetic WB-LGE images was compared with the truly acquired WB-LGE. RESULTS: The CycleGAN architecture for unpaired image-to-image translation was found to provide the most realistic synthetic WB-LGE images from DB-LGE images. The results showed that it was difficult for visual readers to distinguish if an image was true or synthetic (55% correctly classified). In addition, scar burden quantification with the synthetic data was highly correlated with the analysis of the truly acquired images. Bland-Altman analysis found a mean bias in percentage scar burden between the quantification of the real WB and synthetic white-blood images of 0.44% with limits of agreement from -10.85% to 11.74%. The mean image quality of the real WB images (3.53/5) was scored higher than the synthetic white-blood images (3.03), P = 0.009. CONCLUSIONS: This study proposed a CycleGAN model to generate synthetic WB-LGE from DB-LGE images to allow assessment of both image contrasts without additional scan time. This work represents a clinically focused assessment of synthetic medical images generated by artificial intelligence, a topic with significant potential for a multitude of applications. However, further evaluation is warranted before clinical adoption.

Ethnicity differences in geometric remodelling and myocardial composition in hypertension unveiled by cardiovascular magnetic resonance.

AIMS: Hypertensive patients of African ancestry (Afr-a) have higher incidences of heart failure and worse clinical outcomes than hypertensive patients of European ancestry (Eu-a), yet the underlying mechanisms remain misunderstood. This study investigated right (RV) and left (LV) ventricular remodelling alongside myocardial tissue derangements between Afr-a and Eu-a hypertensives. METHODS AND RESULTS: 63 Afr-a and 47 Eu-a hypertensives underwent multi-parametric cardiovascular magnetic resonance. Biventricular volumes, mass, function, mass/end-diastolic volume (M/V) ratios, T2 and pre-/post-contrast T1 relaxation times, synthetic extracellular volume, and myocardial fibrosis (MF) were measured. 3D shape modelling was implemented to delineate ventricular geometry. LV and RV mass (indexed to body-surface-area) and M/V ratio were significantly greater in Afr-a than Eu-a hypertensives (67.1 ± 21.7 vs. 58.3 ± 16.7 g/m2, 12.6 ± 3.48 vs. 10.7 ± 2.71 g/m2, 0.79 ± 0.21 vs. 0.70 ± 0.14 g/mL, and 0.16 ± 0.04 vs. 0.13 ± 0.03 g/mL, respectively; P < 0.03). Afr-a patients showed greater basal interventricular septum thickness than Eu-a patients, influencing LV hypertrophy and RV cavity changes. This biventricular remodelling was associated with prolonged T2 relaxation time (47.0 ± 2.2 vs. 45.7 ± 2.2 ms, P = 0.005) and higher prevalence (23% vs. 4%, P = 0.001) and extent of MF [2.3 (0.6-14.3) vs. 1.6 (0.9-2.5) % LV mass, P = 0.008] in Afr-a patients. Multivariable linear regression showed that modifiable cardiovascular risk factors and greater end-diastolic volume, but not ethnicity, were independently associated with greater LV mass. CONCLUSION: Afr-a hypertensives had distinctive biventricular remodelling, including increased RV mass, septal thickening and myocardial tissue abnormalities compared with Eu-a hypertensives. From this study, modifiable cardiovascular risk factors and ventricular geometry, but not ethnicity, were independently associated with greater LV myocardial mass.

Quantification of myocardial scar of different etiology using dark- and bright-blood late gadolinium enhancement cardiovascular magnetic resonance.

Dark-blood late gadolinium enhancement (LGE) has been shown to improve the visualization and quantification of areas of ischemic scar compared to standard bright-blood LGE. Recently, the performance of various semi-automated quantification methods has been evaluated for the assessment of infarct size using both dark-blood LGE and conventional bright-blood LGE with histopathology as a reference standard. However, the impact of this sequence on different quantification strategies in vivo remains uncertain. In this study, various semi-automated scar quantification methods were evaluated for a range of different ischemic and non-ischemic pathologies encountered in clinical practice. A total of 62 patients referred for clinical cardiovascular magnetic resonance (CMR) were retrospectively included. All patients had a confirmed diagnosis of either ischemic heart disease (IHD; n = 21), dilated/non-ischemic cardiomyopathy (NICM; n = 21), or hypertrophic cardiomyopathy (HCM; n = 20) and underwent CMR on a 1.5 T scanner including both bright- and dark-blood LGE using a standard PSIR sequence. Both methods used identical sequence settings as per clinical protocol, apart from the inversion time parameter, which was set differently. All short-axis LGE images with scar were manually segmented for epicardial and endocardial borders. The extent of LGE was then measured visually by manual signal thresholding, and semi-automatically by signal thresholding using the standard deviation (SD) and the full width at half maximum (FWHM) methods. For all quantification methods in the IHD group, except the 6 SD method, dark-blood LGE detected significantly more enhancement compared to bright-blood LGE (p < 0.05 for all methods). For both bright-blood and dark-blood LGE, the 6 SD method correlated best with manual thresholding (16.9% vs. 17.1% and 20.1% vs. 20.4%, respectively). For the NICM group, no significant differences between LGE methods were found. For bright-blood LGE, the 5 SD method agreed best with manual thresholding (9.3% vs. 11.0%), while for dark-blood LGE the 4 SD method agreed best (12.6% vs. 11.5%). Similarly, for the HCM group no significant differences between LGE methods were found. For bright-blood LGE, the 6 SD method agreed best with manual thresholding (10.9% vs. 12.2%), while for dark-blood LGE the 5 SD method agreed best (13.2% vs. 11.5%). Semi-automated LGE quantification using dark-blood LGE images is feasible in both patients with ischemic and non-ischemic scar patterns. Given the advantage in detecting scar in patients with ischemic heart disease and no disadvantage in patients with non-ischemic scar, dark-blood LGE can be readily and widely adopted into clinical practice without compromising on quantification.

Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.

OBJECTIVES: To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI. MATERIALS AND METHODS: Retrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4. RESULTS: The best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data. CONCLUSION: A deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development. CLINICAL RELEVANCE STATEMENT: A robust automated inversion time selection tool for late gadolinium-enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection. KEY POINTS: • A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.

High Prevalence of New Clinically Significant Findings in Patients With Embolic Stroke of Unknown Source Evaluated by Cardiac Magnetic Resonance Imaging.

BACKGROUND: Embolic stroke of unknown source (ESUS) accounts for 1 in 6 ischemic strokes. Current guidelines do not recommend routine cardiac magnetic resonance (CMR) imaging in ESUS, and beyond the identification of cardioembolic sources, there are no data assessing new clinical findings from CMR in ESUS. This study aimed to assess the prevalence of new cardiac and noncardiac findings and to determine their impact on clinical care in patients with ESUS. METHODS AND RESULTS: In this prospective, multicenter, observational study, CMR imaging was performed within 3 months of ESUS. All scans were reported according to standard clinical practice. A new clinical finding was defined as one not previously identified through prior clinical evaluation. A clinically significant finding was defined as one resulting in further investigation, follow-up, or treatment. A change in patient care was defined as initiation of medical, interventional, surgical, or palliative care. From 102 patients recruited, 96 underwent CMR imaging. One or more new clinical findings were observed in 59 patients (61%). New findings were clinically significant in 48 (81%) of these patients. Of 40 patients with a new clinically significant cardiac finding, 21 (53%) experienced a change in care (medical therapy, n=15; interventional/surgical procedure, n=6). In 12 patients with a new clinically significant extracardiac finding, 6 (50%) experienced a change in care (medical therapy, n=4; palliative care, n=2). CONCLUSIONS: CMR imaging identifies new clinically significant cardiac and noncardiac findings in half of patients with recent ESUS. Advanced cardiovascular screening should be considered in patients with ESUS. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04555538.

Rethinking False Positive Exercise Electrocardiographic Stress Tests by Assessing Coronary Microvascular Function.

BACKGROUND: Exercise electrocardiographic stress testing (EST) has historically been validated against the demonstration of obstructive coronary artery disease. However, myocardial ischemia can occur because of coronary microvascular dysfunction (CMD) in the absence of obstructive coronary artery disease. OBJECTIVES: The aim of this study was to assess the specificity of EST to detect an ischemic substrate against the reference standard of coronary endothelium-independent and endothelium-dependent microvascular function in patients with angina with nonobstructive coronary arteries (ANOCA). METHODS: Patients with ANOCA underwent invasive coronary physiological assessment using adenosine and acetylcholine. CMD was defined as impaired endothelium-independent and/or endothelium-dependent function. EST was performed using a standard Bruce treadmill protocol, with ischemia defined as the appearance of ≥0.1-mV ST-segment depression 80 ms from the J-point on electrocardiography. The study was powered to detect specificity of ≥91%. RESULTS: A total of 102 patients were enrolled (65% women, mean age 60 ± 8 years). Thirty-two patients developed ischemia (ischemic group) during EST, whereas 70 patients did not (nonischemic group); both groups were phenotypically similar. Ischemia during EST was 100% specific for CMD. Acetylcholine flow reserve was the strongest predictor of ischemia during exercise. Using endothelium-independent and endothelium-dependent microvascular dysfunction as the reference standard, the false positive rate of EST dropped to 0%. CONCLUSIONS: In patients with ANOCA, ischemia on EST was highly specific of an underlying ischemic substrate. These findings challenge the traditional belief that EST has a high false positive rate.

Echocardiography versus Cardiac MRI for Measurement of Left Ventricular Ejection Fraction in Individuals with Cancer and Suspected Cardiotoxicity.

Purpose To compare left ventricular ejection fraction (LVEF) measured with echocardiography and cardiac MRI in individuals with cancer and suspected cardiotoxicity and assess the potential effect on downstream clinical decision-making. Materials and Methods In this prospective, single-center observational cohort study, participants underwent same-day two-dimensional (2D) echocardiography and cardiac MRI between 2011 and 2021. Participants with suboptimal image quality were excluded. A subset of 74 participants also underwent three-dimensional (3D) echocardiography. The agreement of LVEF derived from each modality was assessed using Bland-Altman analysis and at relevant thresholds for cardiotoxicity. Results A total of 745 participants (mean age, 60 years ± 5 [SD]; 460 [61.7%] female participants) underwent same-day echocardiography and cardiac MRI. According to Bland-Altman analysis, the mean bias was -3.7% ± 7.6 (95% limits of agreement [LOA]: -18.5% to 11.1%) for 2D echocardiography versus cardiac MRI. In 74 participants who underwent cardiac MRI, 3D echocardiography, and 2D echocardiography, the mean LVEFs were 60.0% ± 10.4, 58.4% ± 9.4, and 57.2% ± 8.9, respectively (P < .001). At the 50% LVEF threshold for detection of cardiotoxicity, there was disagreement for 9.3% of participants with 2D echocardiography and cardiac MRI. Agreement was better with 3D echocardiography and cardiac MRI (mean bias, -1.6% ± 6.3 [95% LOA: -13.9% to 10.7%]) compared with 2D echocardiography and cardiac MRI (mean bias, -2.8% ± 6.3 [95% LOA: -15.2% to 9.6%]; P = .016). Conclusion Two-dimensional echocardiography had variations of ±15% for LVEF measurement compared with cardiac MRI in participants with cancer and led to misclassification of approximately 10% of participants for cardiotoxicity detection. Three-dimensional echocardiography had better agreement with cardiac MRI and should be used as first-line imaging. Keywords: Echocardiography, MR Functional Imaging, Cardiac Supplemental material is available for this article. © RSNA, 2024.

High-resolution quantification of stress perfusion defects by cardiac magnetic resonance.

Aims: Quantitative stress perfusion cardiac magnetic resonance (CMR) is becoming more widely available, but it is still unclear how to integrate this information into clinical decision-making. Typically, pixel-wise perfusion maps are generated, but diagnostic and prognostic studies have summarized perfusion as just one value per patient or in 16 myocardial segments. In this study, the reporting of quantitative perfusion maps is extended from the standard 16 segments to a high-resolution bullseye. Cut-off thresholds are established for the high-resolution bullseye, and the identified perfusion defects are compared with visual assessment. Methods and results: Thirty-four patients with known or suspected coronary artery disease were retrospectively analysed. Visual perfusion defects were contoured on the CMR images and pixel-wise quantitative perfusion maps were generated. Cut-off values were established on the high-resolution bullseye consisting of 1800 points and compared with the per-segment, per-coronary, and per-patient resolution thresholds. Quantitative stress perfusion was significantly lower in visually abnormal pixels, 1.11 (0.75-1.57) vs. 2.35 (1.82-2.9) mL/min/g (Mann-Whitney U test P < 0.001), with an optimal cut-off of 1.72 mL/min/g. This was lower than the segment-wise optimal threshold of 1.92 mL/min/g. The Bland-Altman analysis showed that visual assessment underestimated large perfusion defects compared with the quantification with good agreement for smaller defect burdens. A Dice overlap of 0.68 (0.57-0.78) was found. Conclusion: This study introduces a high-resolution bullseye consisting of 1800 points, rather than 16, per patient for reporting quantitative stress perfusion, which may improve sensitivity. Using this representation, the threshold required to identify areas of reduced perfusion is lower than for segmental analysis.

Characterizing Mechanisms of Ischemia in Patients With Myocardial Bridges.

BACKGROUND: Myocardial bridges (MBs) are prevalent and can be associated with acute and chronic ischemic syndromes. We sought to determine the substrates for ischemia in patients with angina with nonobstructive coronary arteries and a MB in the left anterior descending artery. METHODS: Patients with angina with nonobstructive coronary arteries underwent the acquisition of intracoronary pressure and flow during rest, supine bicycle exercise, and adenosine infusion. Coronary wave intensity analysis was performed, with perfusion efficiency defined as accelerating wave energy/total wave energy (%). Epicardial endothelial dysfunction was defined as a reduction in epicardial vessel diameter ≥20% in response to intracoronary acetylcholine infusion. Patients with angina with nonobstructive coronary arteries and a MB were compared with 2 angina with nonobstructive coronary arteries groups with no MB: 1 with coronary microvascular disease (CMD: coronary flow reserve, <2.5) and 1 with normal coronary flow reserve (reference: coronary flow reserve, ≥2.5). RESULTS: Ninety-two patients were enrolled in the study (30 MB, 33 CMD, and 29 reference). Fractional flow reserve in these 3 groups was 0.86±0.05, 0.92±0.04, and 0.94±0.05; coronary flow reserve was 2.5±0.5, 2.0±0.3, and 3.2±0.6. Perfusion efficiency increased numerically during exercise in the reference group (65±9%-69±13%; P=0.063) but decreased in the CMD (68±10%-50±10%; P<0.001) and MB (66±9%-55±9%; P<0.001) groups. The reduction in perfusion efficiency had distinct causes: in CMD, this was driven by microcirculation-derived energy in early diastole, whereas in MB, this was driven by diminished accelerating wave energy, due to the upstream bridge, in early systole. Epicardial endothelial dysfunction was more common in the MB group (54% versus 29% reference and 38% CMD). Overall, 93% of patients with a MB had an identifiable ischemic substrate. CONCLUSIONS: MBs led to impaired coronary perfusion efficiency during exercise, which was due to diminished accelerating wave energy in early systole compared with the reference group. Additionally, there was a high prevalence of endothelial and microvascular dysfunction. These ischemic mechanisms may represent distinct treatment targets.

ChaMP-CMD: A Phenotype-Blinded, Randomized Controlled, Cross-Over Trial.

BACKGROUND: Angina with nonobstructive coronary arteries is a common condition for which no effective treatment has been established. We hypothesized that the measurement of coronary flow reserve (CFR) allows identification of patients with angina with nonobstructive coronary arteries who would benefit from anti-ischemic therapy. METHODS: Patients with angina with nonobstructive coronary arteries underwent blinded invasive CFR measurement and were randomly assigned to receive 4 weeks of amlodipine or ranolazine. After a 1-week washout, they crossed over to the other drug for 4 weeks; final assessment was after the cessation of study medication for another 4 weeks. The primary outcome was change in treadmill exercise time, and the secondary outcome was change in Seattle Angina Questionnaire summary score in response to anti-ischemic therapy. Analysis was on a per protocol basis according to the following classification: coronary microvascular disease (CMD group) if CFR<2.5 and reference group if CFR≥2.5. The study protocol was registered before the first patient was enrolled (International Standard Randomised Controlled Trial Number: ISRCTN94728379). RESULTS: Eighty-seven patients (61±8 years of age; 62% women) underwent random assignment (57 CMD group and 30 reference group). Baseline exercise time and Seattle Angina Questionnaire summary scores were similar between groups. The CMD group had a greater increment (delta) in exercise time than the reference group in response to both amlodipine (difference in delta, 82 s [95% CI, 37-126 s]; P<0.001) and ranolazine (difference in delta, 68 s [95% CI, 21-115 s]; P=0.005). The CMD group reported a greater increment (delta) in Seattle Angina Questionnaire summary score than the reference group in response to ranolazine (difference in delta, 7 points [95% CI, 0-15]; P=0.048), but not to amlodipine (difference in delta, 2 points [95% CI, -5 to 8]; P=0.549). CONCLUSIONS: Among phenotypically similar patients with angina with nonobstructive coronary arteries, only those with an impaired CFR derive benefit from anti-ischemic therapy. These findings support measurement of CFR to diagnose and guide management of this otherwise heterogeneous patient group.

Viability and Outcomes With Revascularization or Medical Therapy in Ischemic Ventricular Dysfunction: A Prespecified Secondary Analysis of the REVIVED-BCIS2 Trial.

Importance: In the Revascularization for Ischemic Ventricular Dysfunction (REVIVED-BCIS2) trial, percutaneous coronary intervention (PCI) did not improve outcomes for patients with ischemic left ventricular dysfunction. Whether myocardial viability testing had prognostic utility for these patients or identified a subpopulation who may benefit from PCI remained unclear. Objective: To determine the effect of the extent of viable and nonviable myocardium on the effectiveness of PCI, prognosis, and improvement in left ventricular function. Design, Setting, and Participants: Prospective open-label randomized clinical trial recruiting between August 28, 2013, and March 19, 2020, with a median follow-up of 3.4 years (IQR, 2.3-5.0 years). A total of 40 secondary and tertiary care centers in the United Kingdom were included. Of 700 randomly assigned patients, 610 with left ventricular ejection fraction less than or equal to 35%, extensive coronary artery disease, and evidence of viability in at least 4 myocardial segments that were dysfunctional at rest and who underwent blinded core laboratory viability characterization were included. Data analysis was conducted from March 31, 2022, to May 1, 2023. Intervention: Percutaneous coronary intervention in addition to optimal medical therapy. Main Outcomes and Measures: Blinded core laboratory analysis was performed of cardiac magnetic resonance imaging scans and dobutamine stress echocardiograms to quantify the extent of viable and nonviable myocardium, expressed as an absolute percentage of left ventricular mass. The primary outcome of this subgroup analysis was the composite of all-cause death or hospitalization for heart failure. Secondary outcomes were all-cause death, cardiovascular death, hospitalization for heart failure, and improved left ventricular function at 6 months. Results: The mean (SD) age of the participants was 69.3 (9.0) years. In the PCI group, 258 (87%) were male, and in the optimal medical therapy group, 277 (88%) were male. The primary outcome occurred in 107 of 295 participants assigned to PCI and 114 of 315 participants assigned to optimal medical therapy alone. There was no interaction between the extent of viable or nonviable myocardium and the effect of PCI on the primary or any secondary outcome. Across the study population, the extent of viable myocardium was not associated with the primary outcome (hazard ratio per 10% increase, 0.98; 95% CI, 0.93-1.04) or any secondary outcome. The extent of nonviable myocardium was associated with the primary outcome (hazard ratio, 1.07; 95% CI, 1.00-1.15), all-cause death, cardiovascular death, and improvement in left ventricular function. Conclusions and Relevance: This study found that viability testing does not identify patients with ischemic cardiomyopathy who benefit from PCI. The extent of nonviable myocardium, but not the extent of viable myocardium, is associated with event-free survival and likelihood of improvement of left ventricular function. Trial Registration: ClinicalTrials.gov Identifier: NCT01920048.