Circulation: Cardiovascular Imaging最新文献

Background: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.

Methods: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; LVH controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; LVH controls, 222).

Results: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91-0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87-0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90-0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91-0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91-0.95] versus AUC, 0.88 [95% CI, 0.85-0.90]; P<0.001).

Conclusions: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.

Background: Elevated blood pressure (BP) is a major contributor to coronary artery disease. We explored the impact of life-course BP on later-life normalized stress myocardial blood flow (sMBF_N) and myocardial perfusion reserve by cardiovascular magnetic resonance (CMR).

Methods: MyoFit46 prospectively recruited ≈500 National Survey of Health and Development 1946 birth cohort participants, aged ≈77 years, to undergo stress perfusion and late gadolinium enhancement CMR. Systolic (SBPs) and diastolic BPs were recorded at 36, 43, 53, 63, 69, and 77 years. For each participant, the annual rates of BP change (steepness of BP increase) and area under the BP trajectory curve (cumulative life-course BP burden) were derived using mixed-effects models. The associations between BP measures and CMR metrics were tested using generalized linear and additive models, adjusted for antihypertensive use, demographics, lifestyle choices, and comorbidities. Cross-sectional associations between CMR metrics and major adverse cardiovascular events (myocardial infarction, stroke, and heart failure) were also tested. Mediation analyses explored the mechanistic pathways linking life-course BPs, myocardial perfusion, and myocardial fibrosis.

Results: Among 459 included MyoFit46 participants, each 10 mm Hg higher SBP at 36 to 69 years was associated with 3% to 6% lower sMBF_N by CMR at 77 years. At 43 to 63 years, as SBPs rose from 120 to 140 mm Hg, sMBF_N was 18% to 24% lower. Having a sustained higher SBP by 10 mm Hg from 36 to 77 years was associated with 11% (95% CI, 8-14) lower sMBF_N at 77 years. Each 1 mm Hg/y steeper SBP rise during age intervals 36 to 43, 43 to 53, 53 to 63, and 63 to 69 years was associated with 2% to 6% lower sMBF_N at 77 years, associations not conditional on baseline or final BPs in each age interval. Associations may be clinically relevant as each 1% lower sMBF_N was associated with 3% higher odds of major adverse cardiovascular events. sMBF_N mediated ≈20% to ≈40% of the associations between life-course SBPs and late gadolinium enhancement at 77 years. Results were similar for diastolic BP, myocardial perfusion reserve, or sMBF (not normalized).

Conclusions: Higher life-course BPs, steeper increases, and greater cumulative BP burden associate with lower myocardial perfusion by CMR at 77 years, which can be linked with higher odds of major adverse cardiovascular events and greater myocardial fibrosis burden. This underscores the importance of early life BP screening and guiding hyperetension treatment based on longitudinal BP trajectories (rather than relying solely on cross-sectional BPs).

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05455125.

Background: Contractile reserve assessment assesses myocardial performance and prognosis. The microstructural mechanisms that facilitate increased cardiac function have not been described, but can be studied using diffusion tensor cardiovascular magnetic resonance. Resting microstructural contractile function is characterized by reorientation of aggregated cardiomyocytes (sheetlets) from wall-parallel in diastole to a more wall-perpendicular configuration in systole, with the diffusion tensor cardiovascular magnetic resonance parameter E2A defining their orientation, and sheetlet mobility defining the angle through which they rotate. We used diffusion tensor cardiovascular magnetic resonance to identify the microstructural response to dobutamine stress in healthy volunteers and then compared with patients with recovered dilated cardiomyopathy (rDCM).

Methods: In this first-of-its-kind prospective observational study, 20 healthy volunteers and 32 patients with rDCM underwent diffusion tensor cardiovascular magnetic resonance at rest, during dobutamine, and on recovery.

Results: In healthy volunteers, both diastolic and systolic E2A increased with dobutamine stress (13±3° to 17±5°; P<0.001 and 59±11° to 65±7°; P=0.002). Sheetlet mobility remained unchanged (45±11° to 49±10°; P=0.19), but biphasic mean E2A increased (36±6° to 41±4°; P<0.001). In rDCM, diastolic E2A at rest was higher than in healthy volunteers (20±8° versus 13±3°, P<0.001), and sheetlet mobility was reduced (34±12° versus 45±11°; P<0.001). During dobutamine stress, rDCM diastolic and systolic E2A increased compared with rest (20±8° to 24±10°; P=0.001 and 54±13° to 63±11°; P=0.005). However, sheetlet mobility in patients with rDCM failed to increase with dobutamine to healthy levels (39±13° versus 49±10°; P=0.005).

Conclusions: This is the first report describing how the myocardial microstructure facilitates cardiac reserve. In health, sheetlet mobility moves further toward the wall-perpendicular plane to drive increased contractility, rather than increased magnitude of sheetlet mobility. Despite clinical recovery in patients with rDCM, microstructural function at rest and during dobutamine remains impaired. Further understanding of microstructural remodeling at rest and during stress may help refine risk stratification of patients with rDCM at risk of relapse.

Background: This study aims to develop a diffusion model-based framework for generating late gadolinium enhancement (LGE)-like images without contrast. The resulting synthetic images are then comprehensively evaluated for subjective and objective image quality, as well as their clinical utility for quantifying scar in acute myocardial infarction.

Methods: In this retrospective study, we developed a diffusion mode-based framework, multisequence guided diffusion to generate synthetic native enhancement (SNE) images from cine magnetic resonance imaging, and T2 short tau inversion recovery sequences. Data were collected from 331 patients with acute myocardial infarction across 3 centers from January 2014 to July 2024. Subjective and objective image qualities were assessed using Likert scoring and contrast ratio analyses on both internal and external cohorts, comparing SNE with standard LGE to evaluate group differences. Myocardial contours were manually delineated, and scar size and transmurality were quantified using the full-width at half-maximum method to assess the accuracy of myocardial infarction detection.

Results: In comparisons with general generative models and multimodal fusion-based generative approaches, multisequence guided diffusion demonstrated more favorable visual perceptual quality and the closest data distribution alignment to conventional LGE. SNE demonstrated significantly higher quality than LGE (internal: 4.250 [4.000-4.750] versus 4.000 [3.750-4.500]; external: 4.250 [4.000-4.750] versus 4.000 [3.500-4.250]; P<0.05) and improved contrast ratios (blood pool versus myocardium: 9.010 [6.938-12.761] versus 8.767 [6.361-11.745] internally and 16.871 [12.546-24.237] versus 13.472 [9.380-19.599] externally; P<0.05). SNE showed strong agreement with LGE for scar size (internal R=0.839; external R=0.816; P<0.001) and transmurality (internal R=0.792; external R=0.758; P<0.001) with minimal biases (scar size: 2.490% internal, 2.222% external; transmurality: 2.984% internal, 2.225% external), indicating accurate scar depiction and robust generalizability.

Conclusions: SNE demonstrated strong agreement with LGE in quantitative assessment of acute myocardial infarction scar, with comparable or improved image quality.

Heart failure with preserved ejection fraction (HFpEF) is a multifaceted syndrome that often presents diagnostic challenges due to its diverse causes and overlapping symptoms with other conditions. Its prevalence is increasing, driven by an aging population and rising associated comorbidities including obesity, diabetes, and hypertension. Echocardiography is a cornerstone in the screening and diagnosis of HFpEF due to its noninvasive nature, accessibility, and ability to provide a comprehensive cardiac assessment. Cardiac magnetic resonance can further enhance diagnostic accuracy and be a useful tool in follow-up. Cardiac magnetic resonance tissue characterization by parametric mapping sequences (T1/T2 mapping, late gadolinium enhancement, extracellular volume quantification, myocardial flow reserve, and myocardial strain) is also helpful in evaluating specific conditions that can lead to symptoms of heart failure in the setting of normal ejection fraction. The role of cardiac magnetic resonance has become increasingly important with the emergence of new therapies, as distinguishing HFpEF causes is essential for precise therapy selection. In this review, we describe the diagnostic imaging features associated with HFpEF, along with the potential role of imaging in follow-up. We also propose a diagnostic workflow for suspected HFpEF cases in clinical practice.