Computed Tomography Angiography

ObjectivesSevere coronary artery calcification leads to blooming artifacts in conventional coronary CT angiography (CCTA), which may reduce diagnostic accuracy. Ultra-high resolution (UHR) photon-counting detector CT (PCD-CT) reduces calcium blooming. This study aimed to evaluate the diagnostic performance of UHR CCTA with PCD-CT for detecting coronary stenoses in patients with a high coronary calcium burden compared to invasive coronary angiography (ICA).Materials and methodsIn this IRB-approved single-center study, patients with Agatston scores > 600 were included, who underwent ICA and coronary UHR PCD-CT angiography within 2 months. 94% of patients underwent CT prior to transcatheter aortic valve replacement. Coronary arteries were graded for diameter stenosis severity (> 50, > 70%) by two blinded, independent readers. Accuracy, sensitivity, and specificity of CCTA were calculated using ICA as the reference standard. Subgroup analyses were performed across Agatston score groups (600-999, 1000-1999, 2000-2999, and > 3000).Results62 patients (mean age, 76 ± 9 years, 16 women) were included. The median Agatston score was 2423 (IQR: 1085-3340). Across all Agatston categories, CCTA demonstrated high diagnostic accuracy and strong agreement with ICA (Cohen's kappa 0.71-1.00). In patients with Agatston scores > 3000 (n = 18), the diagnostic performance of coronary UHR PCD-CT angiography for detecting > 50% (sensitivity 92-100%, specificity 83-100%) and > 70% diameter stenosis (sensitivity 88-100%, specificity 89-100%) remained high for both readers. There was no correlation between the Agatston score and diagnostic error (p > 0.05).ConclusionUHR CCTA with PCD-CT provides high diagnostic performance even in patients with extensive coronary calcifications.Key pointsQuestion In patients with severe coronary calcification, conventional coronary CT angiography often suffers from blooming artifacts, reducing accuracy and causing unnecessary invasive procedures. Findings Ultra-high resolution coronary angiography with photon-counting detector CT showed high sensitivity and specificity for detecting stenoses, even at Agatston scores > 3000. Clinical relevance Ultra-high resolution coronary CT angiography enables accurate noninvasive detection of stenoses in heavily calcified vessels, potentially reducing invasive coronary angiography.

PurposeDecision-making in thoracic aortic disease primarily relies on diameter thresholds that compress rich three-dimensional morphology into a single length scale. Intrinsic shape measures have been shown to complement diameter, but their utility depends critically on the observation scale imposed during quantification. We aim to identify scales at which a size-invariant shape descriptor, specifically the normalized fluctuation in total integrated Gaussian curvature δK~ , most robustly captures thoracic aortic disease state and to relate the tuned δK~ signal to clinically relevant markers of chronicity in aortic dissection.MethodsWe construct a scale space of aortic surface representations from CTA images via a factorial sweep across smoothing intensity, mesh density, and mesh partitioning (coarse-graining) size. For each construction we compute δK~ , quantify signal robustness and predictive clinical value, and identify a "stable zone" of scales where performance stabilizes. We then model clinical progression using Gaussian Process Regression and relate δK~ to markers of aortic chronicity in dissection, testing whether δK~ behaves as a proxy for phase transitions in disease progression.ResultsA reproducible stable zone emerges in which centimeter-scale partitioning consistently maximizes the informativeness of δK~ while remaining resilient to smoothing, meshing, and acquisition (CT resolution) variability. Within this zone, δK~ augments preoperative risk stratification and, when coupled with Gaussian Process Regression, delineates a nonstationary regime consistent with progressive remodeling in dissection, suggesting linkage to chronicity beyond diameter alone.ConclusionOptimizing scale space yields a tuned, size-invariant shape signal that is both robust and clinically interpretable. The observed association between δK~ and chronicity supports its use as a complementary marker to diameter and motivates prospective validation of shape-aware, curvature-based decision tools.

BackgroundEpicardial adipose tissue (EAT) is a metabolically active visceral fat depot that is both a sensor and a modulator of myocardial biology and changes its composition in response to paracrine signals from the myocardium. We hypothesized that radiomic characterization of EAT from routine coronary computed tomographic angiography (CCTA) can noninvasively capture this adverse remodeling and enable early heart failure (HF) risk stratification.ObjectivesWe sought to develop and externally validate a reproducible radiomic signature of EAT associated with incident HF.MethodsWe conducted a multicenter cohort study of 72,751 adults without known HF or myocardial infarction undergoing CCTA across 9 UK centers (2007-2022). We deployed a fully automated pipeline to segment EAT and extract 1,655 volumetric, shape, and higher-order radiomic texture features. Using a harmonized survival autoencoder architecture, we derived the fat radiomic profile for HF (FRPHF). The model was developed in 59,327 individuals from 7 centers (age 57 ± 13 years, 47.5% female) and externally tested in 13,424 participants from 2 geographically distinct centers (58 ± 12 years, 49.4% female). Survival models were adjusted for age, sex, and conventional risk factors, including coronary artery disease (CAD) severity and EAT volume.ResultsOver a median follow-up of 5.1 and 4.0 years, 1,737 (2.9%) and 363 (2.7%) participants developed HF in the internal and external validation cohorts, respectively. FRPHF demonstrated robust discrimination (C-statistics: 0.869 [95% CI: 0.850-0.889] internal; 0.850 [95% CI: 0.831-0.870] external). Each 25-percentile increase in FRPHF was associated with a nearly 4-fold higher adjusted HF risk (adjusted HRs: 3.90 [95% CI: 3.13-4.84] internal; 3.79 [95% CI: 3.01-4.76] external; both P HF to conventional risk models, including EAT volume and CAD severity, significantly improved 5-year discrimination (ΔAUC: 0.047; 95% CI: 0.029-0.065) and net reclassification (NRI: 0.39; 95% CI: 0.29-0.48) and suggested net clinical benefit on decision curve analysis. The associations were consistent across demographic subgroups and across the ejection fraction spectrum.ConclusionsAutomated radiomic phenotyping of EAT from routine CCTA enables scalable, biologically informed stratification of future HF risk before clinical onset, positioning opportunistic imaging-based visceral fat profiling as a potential tool for precision prevention.