Combined Coronary CT Angiography and CT Perfusion in Coronary Artery Disease (CoroFusion)

Overview

Coronary computed tomography angiography (CTA) provides high-resolution imaging of coronary artery disease (CAD), revealing stenosis, plaque characteristics, and hemodynamic markers like CT-derived fractional flow reserve (CT-FFR), axial plaque stress (APS), and wall shear stress (WSS). However, CTA alone has limitations in evaluating the functional significance of lesions, especially in cases with borderline stenosis, severe calcification, or coronary microvascular dysfunction (CMD). CT myocardial perfusion (CTP) complements CTA by directly assessing myocardial blood flow (MBF) and perfusion reserve (MPR), enhancing diagnostic accuracy. Despite its promise, integrating CTA and CTP for comprehensive CAD assessment remains a challenge. Key gaps include the lack of long-term evidence on combined CTA/CTP findings, particularly in incorporating plaque markers with perfusion deficits. Standardizing ischemic thresholds and validating automated CTP analysis tools remain ongoing challenges. CTP also improves specificity in cases where CTA may overestimate ischemic burden and detects microvascular dysfunction, especially in patients with ischemia and no obstructive CAD (INOCA). Quantitative parameters like MBF have been linked to major adverse cardiovascular events (MACE), but issues with protocol variability and cost-effectiveness persist. This study, a real-world, single-center observational cohort, evaluates the clinical utility of integrated CTA/CTP imaging in CAD management. It will assess diagnostic synergy, impact on clinical decisions, and prognostic value in predicting 5-year MACE. AI-driven imaging analysis will quantify plaque features and myocardial perfusion defects, integrating multimodal parameters to generate individualized ischemia risk scores. The goal is to refine non-invasive diagnostic pathways and improve CAD management strategies.

Coronary computed tomography angiography (CTA) has transformed the non-invasive assessment of coronary artery disease (CAD) by providing high-resolution visualization of coronary stenosis, plaque characteristics, and emerging hemodynamic parameters such as CT-derived fractional flow reserve (CT-FFR), axial plaque stress (APS), and wall shear stress (WSS). Adverse plaque features, including low-attenuation plaque, napkin-ring sign, positive remodeling, and spotty calcification, have been associated with increased ischemic risk and adverse cardiovascular events. However, CTA alone has inherent limitations in evaluating the functional significance of coronary lesions, particularly in cases of borderline stenosis, diffuse atherosclerosis, severe calcification, and coronary microvascular dysfunction (CMD). CT myocardial perfusion (CTP) serves as a functional complement to CTA by directly assessing myocardial blood flow (MBF), myocardial perfusion reserve (MPR), and time-to-peak (TTP), thereby enhancing diagnostic accuracy and risk stratification in CAD. Despite promising results from previous trials, significant gaps remain in integrating CTA and CTP for comprehensive CAD assessment. One of the critical gaps is the limited evidence regarding the long-term prognostic implications of combined CTA/CTP findings, particularly when incorporating plaque vulnerability markers (e.g., lipid-rich necrotic core, pericoronary fat attenuation index) with perfusion deficits. Additionally, the quantification of CTP perfusion defects remains an area of active investigation, with ongoing challenges in standardizing ischemic thresholds, optimizing dynamic versus static protocols, and validating automated analysis tools across diverse populations. Furthermore, insufficient data exist on how an integrated anatomical-functional approach influences clinical decision-making, including revascularization strategies, medical therapy optimization, and long-term outcomes in high-risk subgroups such as patients with diabetes, CMD, in-stent restenosis, and severe coronary calcification. CTP provides unique diagnostic advantages by improving specificity in cases where CTA may overestimate ischemic burden, particularly in moderate stenoses without hemodynamic significance. Moreover, CTP has proven valuable in detecting microvascular dysfunction by identifying impaired MPR in patients with ischemia and no obstructive CAD (INOCA), a population often misclassified by CTA alone. Quantitative perfusion parameters, such as MBF thresholds (\<180 mL/100mL/min), have been independently associated with major adverse cardiovascular events (MACE). However, unresolved issues persist, including protocol variability, limited real-world validation of automated perfusion analysis tools, and unclear cost-effectiveness in routine clinical workflows. This study is a real-world, single-center observational cohort study designed to evaluate the clinical utility of integrated CTA/CTP imaging in CAD management. All treatment decisions will be made jointly by cardiologists and patients based on imaging results and clinical considerations. Patients may undergo medical therapy alone, invasive coronary angiography (ICA) with percutaneous coronary intervention (PCI) when necessary, or additional non-invasive testing such as cardiac magnetic resonance (CMR) or single-photon emission computed tomography (SPECT) for further functional assessment. Our research team will prospectively track the full clinical trajectory of these patients, ensuring comprehensive data collection on diagnostic pathways, therapeutic interventions, and long-term outcomes. At appropriate time points, patients will be stratified into relevant subgroups for detailed analysis. The primary objectives of this study are threefold: (1) to evaluate the diagnostic synergy of combining CTA-derived stenosis severity, plaque morphology, and hemodynamic markers with CTP-derived ischemic parameters against invasive reference standards such as FFR and coronary flow reserve (CFR); (2) to assess the impact of CTA/CTP-guided strategies on clinical decision-making, including revascularization rates, medical therapy adjustments, and lifestyle interventions; and (3) to determine the prognostic value of combined imaging biomarkers in predicting 5-year MACE, with a focus on sex-specific and diabetes-specific thresholds for ischemia detection. In addition, this study will perform subgroup analyses to stratify outcomes in high-risk populations, including patients with diabetes and CMD, post-percutaneous coronary intervention (PCI) cohorts with in-stent restenosis or peri-stent plaque progression, and individuals with severe coronary calcification (Agatston score \>400), where CTA interpretation remains challenging. Methodologically, the study will incorporate AI-driven imaging analysis for both CTA and CTP. Advanced deep learning algorithms will be used to automatically quantify CTA plaque features, including lipid core volume, fibrous cap thickness, and pericoronary adipose tissue attenuation, improving risk stratification beyond conventional stenosis-based assessments. AI-based CTP analysis will enable automated segmentation and quantification of myocardial perfusion defects, standardizing ischemia thresholds and reducing interobserver variability. Furthermore, machine learning models will integrate multimodal imaging parameters-stenosis severity, plaque vulnerability markers, hemodynamic stress metrics (APS, WSS), and perfusion indices-to generate individualized ischemia risk scores, aiding in personalized treatment decision-making. Advanced hemodynamic modeling using finite element analysis will be employed for CT-FFR and APS computation, validated against invasive pressure wire measurements. By addressing existing limitations in CAD imaging, this study aims to establish standardized criteria for interpreting combined CTA/CTP findings, provide robust evidence for CTP-guided risk stratification in understudied populations, define the cost-benefit profile of multimodal imaging, and generate a large-scale imaging registry to support future research in precision cardiovascular medicine. The findings from this study are expected to refine non-invasive diagnostic pathways and contribute to the development of personalized, evidence-based strategies for CAD management.