Circulation: Cardiovascular Imaging最新文献

Background: Accurate aortic stenosis (AS) phenotyping requires multimodality imaging which has limited availability. The digital aortic stenosis severity index (DASSi), an AI biomarker of AS-related remodeling on single-view 2-dimensional echocardiography, predicts AS progression independent of Doppler measurements. We sought to evaluate the ability of DASSi to define personalized AS progression profiles and to validate its performance as a scalable alternative to multimodality imaging features of functional, structural, and biological AS severity.

Methods: In the SALTIRE-2 trial (Study Investigating the Effect of Drugs Used to Treat Osteoporosis on the Progression of Calcific Aortic Stenosis 2) of participants with mild or moderate AS, we performed blinded DASSi measurements (probability of severe AS, 0-1) on baseline transthoracic echocardiograms. We evaluated the association between baseline DASSi and (1) disease severity by hemodynamic (peak aortic valve velocity), structural (computed tomography-derived aortic valve calcium score), and biological features ([¹⁸F]sodium fluoride uptake on positron emission tomography-computed tomography); (2) longitudinal disease progression (change in peak aortic valve velocity and aortic valve calcium score); and (3) incident aortic valve replacement. We used generalized linear mixed or Cox models adjusted for risk factors and aortic valve area.

Results: We analyzed 134 participants (72 [IQR, 69-78] years; 27 [20.1%] women) with a mean baseline DASSi of 0.51 (SD, 0.19). DASSi was independently associated with cross-sectional disease severity: each SD increase was associated with higher peak aortic valve velocity (+0.21 [95% CI, 0.12-0.30] m/s), aortic valve calcium score (+284 [95% CI, 101-467] Agatston units), and [¹⁸F]sodium fluoride target-to-background ratio_max (+0.17 [95% CI, 0.04-0.31]). Higher DASSi was also associated with disease progression by Doppler (peak aortic valve velocity) and computed tomography (aortic valve calcium score) at 24 months (P interaction for DASSi × time<0.001), and future aortic valve replacement (75 events over 5.5 [IQR, 2.4-7.2] years, adjusted HR, 1.42 [95% CI, 1.11-1.84] per SD).

Conclusions: DASSi is associated with functional, structural and biological features of AS severity and predicts disease progression and adverse outcomes. DASSi-enhanced echocardiography may provide an accessible alternative to multimodality AS imaging and serve as a predictive enrichment biomarker in clinical trials.

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02132026.

Percutaneous coronary intervention outcomes rely heavily on accurate lesion assessment and procedural planning. Invasive tools, such as fractional flow reserve, nonhyperemic pressure ratios, intravascular ultrasound, and optical coherence tomography, provide essential physiological and anatomic insights but are resource-intensive, prolong procedures, and increase contrast and radiation exposure. Coronary computed tomography (CT) angiography has emerged as a noninvasive modality with high diagnostic accuracy for coronary artery disease, capable of detailing plaque composition, lesion length, and vessel geometry. With the integration of CT-derived fractional flow reserve and CT myocardial perfusion imaging, coronary CT angiography now offers both anatomic and functional evaluation, bridging diagnostic and interventional decision-making. Despite guideline endorsement for coronary artery disease diagnosis, its role in guiding percutaneous coronary intervention strategies remains underutilized and absent from revascularization recommendations. This review outlines a practical, step-by-step framework for integrating coronary CT angiography into contemporary percutaneous coronary intervention planning, covering acquisition protocols, software platforms, lesion assessment, and stent strategy optimization. It also explores emerging intraprocedural applications, including fusion imaging, augmented and virtual reality, and holographic visualization. By synthesizing current evidence and identifying gaps, this review positions coronary CT angiography as a promising adjunct in precision-based percutaneous coronary intervention.

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.

Background: Depression is linked to major adverse cardiac events (MACE), yet the role of stress-related neural activity-previously implicated in stress and anxiety-in mediating this association remains unclear. Because anxiety and depression frequently co-occur and share neurobiological pathways, we hypothesized that the relationship between depression, anxiety, and their co-occurrence with MACE is partially mediated by increased stress-related neural activity and related autonomic-immune mechanisms.

Methods: Data were obtained from participants enrolled in the Mass General Brigham Biobank (2010-2020). A subset underwent ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography imaging to assess stress-related neural activity, defined as the ratio of amygdala to background prefrontal cortical activity. Heart rate variability and CRP (C-reactive protein) served as indicators of autonomic activity and systemic inflammation. Depression and anxiety were determined at enrollment, and MACE was identified during follow-up using International Classification of Diseases codes. Each exposure (depression, anxiety, or concurrent anxiety plus depression) was modeled separately against study outcomes using linear and Cox regressions.

Results: Of 85 551 study subjects, 3078 (3.6%) participants developed MACE, over a median 3.4 years follow-up (interquartile range, 1.9-4.8). Depression was associated with higher MACE risk (hazard ratio, 1.24 [95% CI, 1.14-1.34]; P<0.001), with stronger associations for concurrent anxiety plus depression (hazard ratio, 1.35 [1.23-1.49]; P<0.001) and remained significant after adjustment for demographics, lifestyle, cardiovascular, and socioeconomic factors. In subsamples with available imaging (N=1123) or biomarkers (heart rate variability, N=7862; CRP, N=12 906), depression was linked to higher amygdala-to-cortex activity ratio (β=0.16; P=0.006), lower heart rate variability (β=-0.20; P<0.001), and higher CRP (β=0.14; P<0.001). Mediation analyses showed indirect effects of amygdala-to-cortex activity ratio, heart rate variability, and CRP on the depression-MACE relationship (log odds ratios, 0.04, 0.04, and 0.02, respectively; all P<0.05). Similar associations were observed for anxiety or concurrent anxiety plus depression.

Conclusions: Depression and anxiety independently associate with increased MACE risk, partly mediated by heightened stress-related neural activity and autonomic-immune dysregulation. The risk is greatest among those with both conditions, underscoring shared stress-related pathophysiology.