Aims:Morphometric analysis of the thoracic aorta (TA) and left ventricle (LV) plays a fundamental role in detecting anatomical abnormalities and functional alterations to support pre-operative planning, predict disease risk and inform device design. However, conventional approaches to morphometric evaluation are typically performed manually using visualization software, thus resulting in time-consuming, operator-dependent processes that are usually limited to static imaging. This work presents an automated three-dimensional image-based methodological framework for dynamic morphometric analysis, from ECG-gated CT datasets. Methods and results:A multi-label 3D U-Net was trained for the automatic segmentation of the TA and LV using a dataset of 50 single-phase CT scans, with ground-truth label maps validated under expert radiological supervision. Model performance was tested on an independent multi-phase cohort of 10 patients. The network achieved high segmentation accuracy, with Dice scores of 97.77 ± 0.31% for the TA and 91.45 ± 1.26% for the LV on the multi-phase test set. The resulting 3D surface models enabled the computation of geometric descriptors, including volumetric indices, displacement fields, and centreline-based diameters, across cardiac phases on 42 patients. Overall, the framework demonstrated robustness to variations in contrast intensity, cardiac motion, and inter-patient anatomical variability, providing a reliable and reproducible pipeline for comprehensive, three-dimensional, and time-resolved morphometric analysis of the ventriculo-arterial complex with physiological or mildly altered anatomy. Conclusion:This approach has strong potential for future clinical translation, supporting quantitative assessment of cardiac function, and aortic pathophysiology.

Carotid atherosclerosis is a major contributor to ischemic stroke. While luminal stenosis has historically guided treatment decisions, growing evidence indicates that plaque composition, vascular inflammation and perivascular adipose tissue (PVAT) may be more closely linked to clinical outcomes and plaque vulnerability. This study aimed to characterize carotid PVAT using photon-counting computed tomography (PCCT) and to evaluate its spatial behavior and variability in a cohort of asymptomatic patients. We retrospectively analyzed PCCT angiography data from 20 asymptomatic patients. A custom-developed Python algorithm was used to segment concentric perivascular layers from 1 mm to 5 mm around the carotid artery. For each layer, we quantified attenuation values in Hounsfield Units (HU) and voxel counts. Statistical comparisons were performed across layers and between sides. Mean PVAT attenuation decreased progressively with increasing distance from the carotid wall. Significant differences were observed between inner and outer layers, particularly between the 1 mm and 3–5 mm annuli. Circle-by-circle analysis revealed substantial inter-individual variability in HU trends. Voxel count increased with annular thickness, but variability (SD and CV) also rose in outer layers. No significant differences were found between left and right carotid arteries in either attenuation or voxel distribution. Photon-counting CT enables detailed, layer-specific assessment of carotid PVAT. The observed attenuation patterns and inter-individual variability suggest that PVAT profiling may provide valuable insights into local vascular inflammation and plaque vulnerability. These findings support the potential of PCCT as a noninvasive tool for vascular risk stratification beyond luminal stenosis. Question Can photon-counting CT enable a reliable, layer-by-layer quantitative characterization of carotid perivascular adipose tissue in asymptomatic patients beyond luminal stenosis assessment? Findings Photon-counting CT demonstrated a progressive decrease in PVAT attenuation with increasing distance from the carotid wall and marked inter-individual variability across concentric layers. Clinical relevance Layer-specific PVAT profiling with photon-counting CT may provide a noninvasive imaging marker of local vascular inflammation, supporting improved carotid risk stratification beyond stenosis severity, even in asymptomatic individuals.

BACKGROUND:Despite a significant association of atherogenic index of plasma (AIP) with plasma atherogenicity as well as insulin resistance and new onset of diabetes, data on the risk of rapid plaque progression (RPP) of major epicardial coronary arteries related to AIP according to established diabetes is limited. METHODS:This study evaluated the association between AIP and RPP according to diabetes in 1485 adults (60.9 ​± ​9.2 years, 58.9% men, 23.8% diabetes) using serial coronary computed tomography angiography. AIP was defined as the base 10 logarithm of the ratio of triglycerides to high-density lipoprotein cholesterol (mmol/L). RPP was defined as a change in percent atheroma volume (PAV) ≥1.0%/year. RESULTS:During a median follow-up of 3.4 years, the incidence of RPP was 26.1%. Compared with non-diabetic subjects, diabetic subjects exhibited significantly higher AIP levels, larger baseline plaque burden, and higher RPP incidence. After adjusting for age, sex, hypertension, overweight or obesity, current smoking, low-density lipoprotein cholesterol and creatinine levels, baseline total PAV, and the use of aspirin, beta-blockers, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, or statins, AIP (per 0.1-unit increase) was associated with the risk of RPP in non-diabetic subjects (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.01-1.13; p ​< ​0.05) but not in diabetic subjects (OR 1.04, 95% CI 0.95-1.14; p ​= ​0.430). CONCLUSION:AIP is independently associated with the risk of RPP in the absence of established diabetes. This finding suggests that AIP may be an effective biomarker for predicting RPP in the population of non-diabetic adults. TRIAL REGISTRATION:ClinicalTrials.govNCT02803411.

The latest technological advancements in CT enable the exploration of unprecedented limits of spatial resolution in in vivo imaging. Nowadays, ultra-high-resolution imaging is available by using CT with detector elements at or smaller than 0.25 mm along the z-axis, like those used on photon-counting CT (PCCT) scanners. However, spatial resolution represents a complex criterion of imaging performance affected not only by detector elements, but also by other complex variables that can interact with each other. Knowledge of these variables and the metrics to evaluate spatial resolution is key to performing accurate cardiothoracic examinations with optimized CT protocols, which can eventually reduce acquisition times and radiation doses. This opens to a sustainable cardiothoracic radiology that permits accurate cardiac CT evaluations also in patients previously excluded, due to high calcium score, metallic stents or obesity, and allows to reduce radiation doses to never-seen levels. In this article, we review the technical advancements that allowed such an increase in spatial resolution in PCCT, along with all technical determinants of spatial resolution, the metrics to evaluate it, the clinical impact of UHR at PCCT and its challenges on cardiothoracic imaging. Knowledge of the ultra-high spatial resolution capabilities of new photon-counting CT technology is key to its best uses — performing accurate diagnostic examinations at unmatched low radiation doses and scanning patients previously excluded from cardiac CT examinations.

Background/Objectives: Cardiac magnetic resonance (CMR) imaging at 7 Tesla provides a substantially higher intrinsic signal-to-noise ratio compared with conventional 1.5 T and 3 T systems, potentially enabling higher spatial resolution, improved tissue contrast, and advanced metabolic imaging. However, clinical translation remains limited by technical challenges associated with ultra-high-field operation. This systematic review aimed to synthesize current human in vivo evidence on the feasibility, applications, and methodological limitations of 7-T cardiovascular MRI. Methods: A PRISMA-guided systematic search of PubMed, Cochrane Library, Web of Science, and Scopus was conducted from database inception through January 2025. Studies reporting human in vivo cardiovascular MRI at 7 Tesla were included. Data regarding study design, sample characteristics, imaging applications, feasibility, quantitative findings, and reported limitations were extracted and qualitatively synthesized. Results: Sixty-five studies met inclusion criteria, predominantly small prospective cohorts (mean sample size = 13), largely involving healthy volunteers. Across diverse applications—including coronary MR angiography, cine imaging, valvular assessment, vascular imaging, flow quantification, myocardial tissue characterization, and multinuclear (31P, 23Na, 39K) imaging—7-T CMR was consistently feasible and capable of producing high-quality images. Quantitative ventricular and vascular measurements were generally concordant with lower field strengths. Incremental benefits were most apparent in high-resolution structural imaging and metabolic applications, whereas routine functional and flow assessments showed limited additional advantages. No serious adverse events were reported. Conclusions: Human cardiovascular MRI at 7 Tesla represents a technically feasible research and early translational platform with selective advantages over established field strengths. Further advances in radiofrequency technology, protocol harmonization, and larger disease-focused studies are required to clarify its potential clinical role.

This phantom study presents a thorough characterization of the physical image quality of a clinical whole-body photon-counting computed tomography (PCCT) scanner. Multiple quality metrics—noise, noise power spectrum (NPS), task transfer function (TTF), and detectability index (d′)—were analyzed across a range of reconstruction algorithms (filtered back projection, FBP, and Quantum Iterative Reconstruction, QIR, with strength levels Q1–Q4), and varying reconstruction kernels (Br40/Br60/Br76/Br98). Both standard (STD, 0.4 mm slice thickness) and high-resolution (HR, 0.2 mm slice thickness) reconstruction modes were assessed. QIR significantly reduced image noise (60–95%) compared to FBP, particularly with sharper kernels. Spatial resolution improved with increasing QIR strength level for smoother kernels and was further enhanced using HR mode with sharp kernels. HR mode exhibited better noise performance than STD with sharper reconstructions, due to the small pixel effect. While STD mode showed higher d′ values for larger objects, HR mode outperformed it for smaller objects and sharper kernels. Compared to a conventional energy-integrating computed tomography system, the PCCT scanner showed superior d′ values under similar settings. Overall, this study highlights the complex interplay between acquisition and reconstruction parameters on image quality, confirms the potential of PCCT technology, and underscores the need for further clinical validation.

Aims:Metabolic syndrome (Mes) and diabetes are emerging cardiometabolic determinants of coronary atherosclerotic disease (CAD) risk besides LDL-cholesterol (LDL-C) and established risk factors. We aimed to assess whether, in patients with chronic coronary syndrome (CCS), cardiometabolic risk is prevalent and independently associated with residual CAD risk in subjects with low LDL-C. Methods and results:The cross-sectional HURRICANE study (Health improvement by Understanding RR In CAd and NEw targets for treatment) included 479 patients with CCS (mean age 65 ± 11 years, 70% male), undergoing cardiac computed tomography angiography (CCTA). A severe/extensive CAD or a moderate-high CAD risk were defined, on patient level, by CCTA-derived CAD-RADS2 and Leiden scores. Metabolic syndrome was present in 31% of patients, diabetes or pre-diabetes in 21% and 26%, severe/extensive CAD and moderate-high Leiden score in 51% and in 61%, more frequently in the two lower LDL-C categories. Multivariate logistic regression models, included age, sex, smoking status, family history, LDL-C categories (<70, 70-99 100-129, and ≥130 mg/dL), MeS, or its components and medications. Independent predictors of moderate-high Leiden score were age, male sex, the lowest LDL-C category and MeS (OR 2.12, 95% CI: 1.26-3.56) or pre-diabetes (OR 1.90, 95% CI: 1.12-3.21) and diabetes (OR 6.13, 95% CI: 1.93-19.45). In the lowest LDL-C group, higher CAD-RADS2 and Leiden scores were more frequent in patients with cardiometabolic risk. Conclusion:Results of this cross-sectional study suggest that dysregulation of glucose metabolism is the prevalent component of residual cardiometabolic and coronary atherosclerotic risk in patients with CCS and low LDL-C under current treatment.

Introduction The totality of atherosclerosis from the coronary tree can be measured with quantitative coronary CT and is increasingly being recognized as important marker for future cardiovascular events. We evaluated the risk and absolute event rates of plaque volume staging systems and provided analyses according to the clinical likelihood of obstructive CAD. Methods CONFIRM2 is an ongoing, global, multicenter, observational registry that included patients with clinically indicated coronary CT angiography and follow-up for major adverse cardiovascular events (MACE). Patients without cardiac symptoms and prior CAD were excluded. Atherosclerosis was quantified across all coronary segments by AI-QCT. Total plaque volume (TPV) was categorized into non-calcified plaque (NCPV, HU <350) and calcified (HU>350). The primary end point was the incidence rate of MACE over at 4 years and included all-cause mortality, myocardial infarction, stroke, congestive heart failure, late revascularizations (occurring >90 days post index CCTA), and hospitalization for unstable angina. Secondary endpoint included Death, MI, and stroke. The prognostic value of TPV and NCPV was evaluated across the pretest likelihood according to the ESC Risk Factor Weighted Likelihood (ESC RF-CL). Results A total of 6061 patients (mean age 58.6 ± 12.1 years, 48.6% male) were included and the mean follow-up duration was: 4.4 ± 1.8 years. According to the ESC RF-CL for obstructive CAD, patients were classified as very-low, low, and moderate risk in 36.0%, 43.0%, and 22.0%, and obstructive CAD rates were 6.8%, 15.7%. and 28.2, respectively. Within the total cohort, TPV >0–250, 250–750, and >750 mm3 were associated with MACE: HR 2.5 (95% CI 1.1, 5.6), HR 10.1 (95% CI 4.4, 22.9), and HR 15.9 (95% CI 6.7, 38.0) compared with TPV=0. According to RF-CL very-low, low, and moderate, the MACE rates were: 0.7, 2.9, 0.0% for TPV=0 mm3; 2.3%, 3.6%, 4.6% for TPV >0–250 mm3; 10.3%, 12.3%, 13.5% for TPV 250–750 mm3; and 18.8%, 16.7%, and 19.8% for TPV>750 mm3. Findings were consistent for the secondary endpoint. Conclusion Among a large cohort of symptomatic patients evaluated by coronary CT angiography, the burden of atherosclerosis by AI-QCT was the main driver for cardiovascular events. Absolute event rates were consistently higher by each plaque volume stage, and consistent across the clinical likelihood subgroups for obstructive CAD. While quantitative atherosclerosis was present in the majority of patients, risk increased significantly from 250 mm3 of TPV during intermediate term follow-up.

Purpose To assess the association of low-attenuation noncalcified plaque (LAP) morphologic features, including shape and degree of intraplaque embeddedness, using atherosclerotic imaging-enabled quantitative CT with acute coronary syndrome (ACS). Materials and Methods In this secondary analysis of the Incident Coronary Syndromes Identified by CT (ICONIC) study, a retrospective-nested, case-control, multicenter study of patients with future ACS after coronary CT angiography propensity matched with controls, atherosclerotic imaging-enabled quantitative CT analysis was performed between February and September 2022. LAP morphology was determined qualitatively according to geometric shape (crescent, lobular, spherical, or bean) and degree of intraplaque embeddedness (<90°, 90°-179°, 180°-269°, 270°-360°) within the vessel wall. Shapes were based on visual assessment of LAP contours, including features of curvature, symmetry, and lobulated and protruding components. Adverse LAP morphology (ALM) was defined by morphologic features associated with ACS using log-rank testing. Multivariable Cox regression was performed with ALM as the independent variable, adjusting for plaque burden and diameter stenosis. Results A total of 446 patients (mean age ± SD, 62.44 years ± 11.05; 277 male; 223 cases, 223 controls) with a mean follow-up of 2.45 years ± 2.49 were included in this study. Twenty-two patients were excluded due to missing images or poor image quality. Lobular, bean, and spherical LAP shape or LAP with a degree of intraplaque embeddedness greater than or equal to 180° was associated with increased ACS risk (log-rank P < .05 for each). These morphologic features thus defined ALM. Among 74 of 446 (16.6%) patients with ALM, 57 patients experienced ACS. Patients with ALM had a 3.24-fold increased risk for ACS (adjusted hazard ratio, 3.24 [95% CI: 1.44, 7.30]; P = .005). Conclusion ALM was independently associated with ACS. Keywords: Coronary Angiography, Coronary Arteries ClinicalTrials.gov identifier: NCT02959099 Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.

BACKGROUND:Plaque assessment by quantitative coronary CT angiography has demonstrated to correlate highly with intravascular ultrasound and optical coherence tomography, and these modalities have shown strong prognostic value. OBJECTIVES:The purpose of this study was to identify the prognostic value of artificial intelligence-guided quantitative CCTA (AI-QCT) for major adverse cardiovascular events (MACE) against the risk factor-weighted clinical likelihood model. METHODS:The CONFIRM2 (COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is a multicenter, international, observational cohort study that included patients with clinically indicated CCTA and follow-up for MACE. Patients without cardiac symptoms and prior coronary artery disease (CAD) were excluded. Across the entire coronary artery tree, the presence, extent, and composition of CAD were analyzed by an AI-QCT software, and 24 variables at a patient, vessel, and plaque level were derived, including percent luminal narrowing, remodeling index, plaque volumes (total, calcified, noncalcified, low attenuation), and plaque composition. The primary MACE endpoint was defined as a composite of all-cause death, myocardial infarction (MI), stroke, congestive heart failure, late revascularizations, and hospitalization for unstable angina. The secondary MACE endpoint was defined as all-cause death and MI. RESULTS:A total of 3,551 patients (age 58.8 ± 12.5 years, 50.5% male) were followed for a median of 4.27 (IQR: 3.47-5.08) years during which 167 (4.7%) events occurred. After excluding collinear variables, diameter stenosis (HR: 1.25 [95% CI: 1.18-1.32]) per 10% increase and noncalcified plaque volume (HR: 1.07 [95% CI: 1.03-1.11]) per 50 mm3 increase were the only independent predictors for MACE. In multivariable modeling, the discriminatory value defined by area under the curve (AUC) improved from 0.63 (95% CI: 0.58-0.67) based on the risk factor-weighted clinical likelihood model to 0.76 (95% CI: 0.77-0.80), P < 0.001 when adding AI-QCT-based diameter stenosis and noncalcified plaque volume. A similar improvement in risk prediction was seen when adding AI-QCT (AUC 0.77; P < 0.001) to a model with traditional risk factors, age, and sex (AUC: 0.67). In addition, AI-QCT significantly improved discrimination compared to the atherosclerotic cardiovascular disease risk score (AUC: 0.63; 95% CI: 0.58-0.68) to 0.75 (95% CI: 0.69-0.80; P < 0.001). Similar results were seen for the secondary MACE endpoint of death/MI. CONCLUSIONS:This first multicenter global registry with AI-guided quantitative CT identified noncalcified plaque burden and increment in stenosis severity as the most powerful predictors of MACE, demonstrating the interplay between traditional and novel measures of the severity of CAD. Standardized and rapid quantitative assessment of CAD may improve clinical implementation of multidimensional assessment of CAD as a cornerstone for risk assessment.

BACKGROUND:Statins are a cornerstone medication for coronary atherosclerosis. This study assessed whether radiomic analysis of coronary computed tomography angiography (CCTA) could predict patient response to statin therapy. METHODS:Patients from a multinational registry with serial CCTA (≥2-year intervals) on statin therapy were analyzed. Radiomic scores were calculated, categorizing patients as statin responders or non-responders (≥1.0% increase in percent atheroma volume (PAV) per year indicated non-response). Data were split into training (79%) and test (21%) sets based on sites. Four predictive models were developed: Model 1 used clinical risk factors (CRF), Model 2 included CRF, calcified and non-calcified PAV, and number of high-risk plaques. Model 3 used only the radiomic score, and Model 4 combined Models 2 and 3. RESULTS:A total of 386 statin responders (mean age 61.2 ​± ​8.4 years, 60.1% male) and 177 statin non-responders (mean age 63.2 ​± ​9.0 years, 44.1% male) were analyzed. Model 3, based solely on the radiomic score, demonstrated superior predictive power compared to Model 1 (area under the receiver operating characteristic curve [AUC] [95% confidence interval (CI)]: 0.75 [0.67-0.83] vs 0.54 [0.45-0.63], p ​< ​0.05) and was comparable to Model 2 (AUC [95% CI]: 0.82 [0.74-0.88], p ​> ​0.05) in the test set. Model 4 exhibited the highest power (AUC [95% CI]: 0.84 [0.77-0.90], all p ​< ​0.05 compared to Model 2 and Model 3). CONCLUSION:CCTA radiomic features show proof-of-concept for predicting statin response, warranting further validation. CLINICAL TRIAL REGISTRATION:ClinicalTrials.gov NCT0280341.

Rapid progression of coronary atherosclerosis is associated with an increased risk of future adverse cardiovascular events. However, evidence regarding the association between glycemic status and rapid plaque progression (RPP) in the major epicardial coronary arteries remains limited. A total of 1296 subjects (mean age, 61 ± 9 years; 56.9

Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. However, most current analysis methods for arterial 4D flow MRI use static artery walls because of the difficulty in obtaining a full cycle segmentation. To overcome this limitation, we propose a neural fields-based method that directly estimates continuous periodic wall deformations throughout the cardiac cycle. For a 3D + time imaging dataset, we optimize an implicit neural representation (INR) that represents a time-dependent velocity vector field (VVF). An ODE solver is used to integrate the VVF into a deformation vector field (DVF), that can deform images, segmentation masks, or meshes over time, thereby visualizing and quantifying local wall motion patterns. To properly reflect the periodic nature of 3D + time cardiovascular data, we impose periodicity in two ways. First, by periodically encoding the time input to the INR, and hence VVF. Second, by regularizing the DVF. We demonstrate the effectiveness of this approach on synthetic data with different periodic patterns, ECG-gated CT, and 4D flow MRI data. The obtained method could be used to improve 4D flow MRI analysis.

CT acquisition parameters and reconstruction techniques may affect the accuracy of calcium scoring measurements with a potential impact on clinical decision making. We evaluated the agreement of half- versus standard-dose protocols for assessment of aortic valve (AVCS HD and AVCS SD protocols) and coronary artery calcium scoring (CACS HD and CACS SD protocols) with and without the application of iterative reconstruction. We enrolled 144 consecutive patients (mean age 83 ± 9 years) with known aortic stenosis undergoing 128-row prospective sequential CT with standard (120 kVp/20 mAs) and half-dose (120 kVp/10 mAs) protocols for both AVCS and CACS evaluation. The half-dose dataset was processed with and without iterative reconstruction. Agreement and precision of different protocols were evaluated using linear regression and Bland–Altman analysis. Additionally, we assessed the reclassification of cardiovascular risk based on the Mayo Clinic system and the likelihood of severe aortic stenosis using sex-specific categories. Compared with the standard dose, the half-dose protocol with or without iterative reconstruction demonstrated optimum agreement for the evaluation of AVCS (r = 0.99; R2 = 0.97) and CACS (r = 0.96; R2 = 0.93). The half-dose iterative reconstruction protocol yielded a very low rate of reclassification aortic stenosis severity (1.4

BACKGROUND:Coronary plaque features are imaging biomarkers of cardiovascular risk, but less is known about sex-specific patterns in their prognostic value. This study aimed to define sex differences in the coronary atherosclerotic phenotypes assessed by artificial intelligence-based quantitative computed tomography (AI-QCT) and the associated risk of major adverse cardiovascular events (MACEs). METHODS:Global multicenter registry including symptomatic patients with suspicion of coronary artery disease referred for coronary computed tomography angiography. AI-QCT analyzed 16 coronary artery disease features. The primary end point was MACE defined as death, myocardial infarction, late revascularization, cerebrovascular events, unstable angina, and congestive heart failure. RESULTS:Among 3551 patients (mean age, 59±12 years; 49.5% women), MACE occurred in 3.2% of women and 6.1% of men during an average follow-up of 4.8±2.2 years. The AI-QCT features total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume were significantly higher in men (P<0.001), and high-risk plaques were more prevalent (9.2% versus 2.5%; P<0.0001). Independent of age and cardiovascular risk factors, the AI-QCT-derived features of total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume conferred a higher relative risk of MACE in women than men. For every 50-mm3 increase in total plaque volume, relative risk increased by 17.7% (95% CI, 1.12-1.24) in women versus 5.3% (95% CI, 1.03-1.07) in men (Pinteraction<0.001); for noncalcified plaque, relative risk increased by 27.1% (95% CI, 1.17-1.38) versus 11.6% (95% CI, 1.08-1.15; Pinteraction=0.0015); and for calcified plaque, relative risk increased by 22.9% (95% CI, 1.14-1.33) versus 5.4% (95% CI, 1.01-1.10; Pinteraction=0.0012), respectively. Similarly, for percentage atheroma volume, the risk was higher in women. The findings remained unchanged when restricted to a secondary composite end point (death and myocardial infarction). CONCLUSIONS:The AI-QCT plaque features, total plaque volume, noncalcified plaque, calcified plaque, and percentage atheroma volume, conferred a higher relative MACE risk in women and may prompt more aggressive antiatherosclerotic therapy and reinforced preventive interventions. REGISTRATION:URL: https://www.clinicaltrials.gov; Unique identifier: NCT04279496.

AI-QCT provided incremental prognostic information compared with CAD-RADS 2.0, CACS, and the modified Duke Index for the prediction of MACE as well as the secondary endpoint of death or nonfatal MI.

Numerical simulations play a key role in evaluating the hemodynamics of the thoracic aorta (TA). Common computational fluid dynamics (CFD) methods apply the rigid wall hypothesis, thus disregarding vessel deformation during the cardiac cycle; Fluid-Structure Interaction (FSI) approaches, while accounting for vessel compliance, demand extensive computational resources and rely on assumptions about wall mechanical properties. This study aims to develop a digital twin model of the aorta by implementing an AI-based framework for patient-specific moving boundaries, to be applied in CFD simulations (CFDMB) of the entire aorta. Starting from multi-phase ECG-gated CT scans, we built models of the TA and left ventricle (LV) at different phases of the cardiac cycle. An in-house non rigid-registration coupled with radial basis functions interpolation, was used to get iso-topological and mapped surface meshes at each phase. From the analysis of the LV volume changes during the cardiac cycle, patient-specific inlet condition was also applied. Results from CFDMB simulations were compared with those obtained from CFD. The CFDMB approach accurately captured TA morphological changes during the cardiac cycle, without compromising mesh quality. Differences in the main hemodynamic results were found between the two performed simulations strategies. The CFDMB approach also modeled the flow waveform shift that occurs along the TA lumen, enabling pulse wave velocity estimation. The implemented pipeline represents a promising method for patient-specific hemodynamic studies, overcoming the limitations of both conventional CFD and FSI simulations.

Pericoronary adipose tissue attenuation, a marker of coronary inflammation, shows minimal quantitative differences between patients with and without future incident acute coronary syndromes in a matched case-control multicenter cohort of patients who underwent coronary CT angiography but is independently associated with future incident acute coronary syndromes in adjusted survival analyses.

Fluid-structure interaction (FSI) can be key in the generation of accurate digital replica of cardiovascular systems. To personalize these models, however, several patient-specific parameters need to be measured, which can be challenging to accomplish in a non-invasive manner. Alternatively, the cardiac kinematics of the patient can be extracted from imaging data and then directly imposed as a dynamic boundary condition in the computational model, also incorporating temporal and spatial measurement errors. A more advanced method combines FSI with kinematic driven simulations using data-assimilation. Despite its potential, the application of this technique to complex multi-physics cardiovascular simulations remains limited. In this study, we develop an FSI model of a patient's left ventricle (LV) and aorta, personalized with dynamic imaging data using a Nudging algorithm-a data assimilation technique-which is tailored to each cardiac chamber. In particular, for the LV, which embeds small-scale and irregular endocardial structures (higher measurement errors), the active contraction of the patient is replicated primarily using integral measurements (ventricular volume and surface area). On the other hand, the passive motion of the aorta is guided in the simulation relying directly on the local tissue positions from CT scan. The algorithm's simplicity and zero additional computational cost make it particularly suitable for multi-physics problems. Our results show that the assimilation procedure must be tuned to guide the system toward the measurements within the uncertainty range of the in-vivo data.