Despite the promising results, the clinical implications of the CCT-FFR is already debated. This metanalysis aimed to determine the potential benefits of incorporating FFRCT into stable CAD management. After searching for studies comparing outcomes of patients with suspected stable CAD who underwent CCT-FFR as a first strategy versus non-urgent cardiovascular testing after a clinical judgment, we calculated odds ratios (ORs) and 95 ​% confidence intervals (CIs) using a random-effects or fixed-effects meta-analysis model depending on heterogeneity significance. 5 studies (3 RCTs and 2 observational studies) globally encompassing 5282 patients (CCT-FFR ​= ​2604 patients, Control Group ​= ​2678 patients) were included in the quantitative analysis. The rates of ICA overall (OR 1.57, 95%CI 1.36-1.81, p value ​< ​0.001) and those without obstructive CAD (OR 6.63, 95%CI 4.79-9.16, p value ​< ​0.001) were reduced in the CCTAFFR group, as compared to the control group. Moreover, CCT-FFR patients underwent coronary revascularization more frequently than patients in the control arm (OR 0.48,CI 0.38-0.62, p value ​< ​0.001). There was no significance difference between the two strategies in terms of 1 year MACE (OR 1.11,CI 0.86-1.44, p value 0.42), nonfatal MI (OR 0.73, CI 0.41-1.33, p value 0.31), all-cause mortality (OR 1.29,CI 0.47-3.54, p value 0.63) and unplanned revascularization for angina (OR 0.99, 95%CI 0.65-1.49, p value 0.95). In conclusion, in the management of stable CAD, the use of CCT-FFR was associated with lower overall rates of ICA but higher rates of coronary revascularization with comparable 1-year clinical impact.

As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCT) has comparable diagnostic performance with CFD-based FFRCT (FFRCT). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCT vs. FFRCT.

Aortic calcification, often incidentally detected during coronary artery calcium (CAC) scans, is underutilized in cardiovascular risk assessments due to manual quantification challenges. This study evaluates a deep learning model for automating aortic calcification detection and quantification in coronary CT angiography (CTA) images. We validate against manual assessments and compare the association of manual and automated assessments with incident major adverse cardiovascular events (MACE).

The association between high-risk plaque (HRP) on coronary computed tomography angiography (CTA) and the level of perivascular inflammation has not been fully investigated.

In an era of rapidly expanding use of transcatheter aortic valve replacement (TAVR), cardiovascular computed tomography (CCT) has become an essential component in the evaluation process for the growing number of patients. Because of the nature of the guideline-recommended protocol -involving several different CCT acquisitions-it represents a unique dataset for comprehensive phenotyping of the patient with significant aortic stenosis. A substantial body of data has established CCT as a central tool in pre-procedural implantation planning. However, emerging evidence suggests a potential new role for CCT in phenotyping patient risk beyond the index procedure. This new role could represent a unique opportunity in patient selection, medication optimization and follow up post TAVR aiming to improve long-term prognosis. This review highlights emerging data on CCT imaging features for risk stratification in patients during long-term follow-up after TAVR. We summarize the existing literature on this topic and explore whether comprehensive CCT-derived information could be integrated into clinical practice, potentially enhancing TAVR patient selection and post-procedural care.

Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS).

Cardiac Computed Tomography (CCT) is increasingly used for evaluation of congenital heart disease (CHD) in patients of all ages. Pediatric and adult congenital heart disease (ACHD) surgical programs require high quality CCT imaging as part of the multimodality imaging support expected of comprehensive care centers. Despite these expectations, there are no benchmarks or defined programmatic elements specific to the performance of CCT in patients with CHD. To address this deficit, this manuscript is written by a group of current CHD CCT practitioners and provides a collective opinion regarding the clinical components required, and essential resources needed, to deliver a comprehensive CCT CHD imaging program. Resource allocation was divided into CCT technology, imaging technologist, physician and programmatic support. The group is inclusive of pediatric and adult cardiologists and radiologists and includes practitioners from high and lower resourced programs and countries. Imaging settings are inclusive of academic and private practice, heart centers and combined radiology/cardiology service lines. Challenges and areas for future advocacy to support this growing specialty are proposed to improve performance standards that will consider the expected widespread variation in technical and staffing resources, skillsets, and practice settings for CT in CHD.

Discrepancies often exist between patient-reported symptoms and diagnostic test findings in patients with suspected obstructive coronary artery disease (CAD).

Cardiac Computed Tomography (CCT) is increasingly used to provide 2D, 3D and 4D information in patients with congenital heart disease of all ages. Historically, negotiated rates for professional and technical fees associated with cardiac imaging were confidential, with variability in professional, technical and global charges, reimbursement and cost to patients for the same current procedural terminology (CPT) code at different institutions. Billing transparency is a key component of both the CARE act passed in 2020 and the Health Care PRICE Transparency Act 2.0 passed in 2021. Institutional technical fees and negotiated insurance rates by CPT billing code are now publicly available and can be compared between institutions.