Coronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. Coronary Computed Tomography angiography (CCTA) gained a pivotal clinical role for excellent sensitivity in rule-out CAD, but has limited specificity for a tendency to overestimate stenoses and for the lack of information about their hemodynamic impact. Fractional Flow Reserve derived from CT (FFR-CT) and stress CT perfusion (CTP) have been recently proposed to complement CCTA in the non-invasive assessment of myocardial ischemia, increasing the specificity and avoiding unnecessary catheterization. However, on energy-integrating (EID)-CT, FFR-CT has suboptimal performance, while CTP is affected by high radiation exposure. Both these approaches may benefit by the introduction of the new Photon Counting Detector (PCD)-CT technology, but data completely lacks. Aim of the study is to assess the performance of PCD-CT in the identification of significant CAD combining CCTA with FFR-CT and spectral CTP.
Study Type
OBSERVATIONAL
Enrollment
142
Azienda Ospedaliero-Universitaria Sant'Andrea
Roma, RM, Italy
IRCCS Ospedale San Raffaele
Milan, Italy
Diagnostic accuracy of CCTA, CCTA plus FFR-CT, and CCTA plus spectral stress CT perfusion in the detection of hemodynamically relevant coronary stenosis
CCTA-derived stenosis and plaque characterization; CT-FFR values from commercial and in-house solutions; CTP-derived iodine uptake measurement; invasive FFR as reference standard. Comparison of off-site versus on-site CT-FFR solutions determined against invasive FFR.
Time frame: 36 months
Evaluation of rest spectral CT perfusion for the assessment of haemodynamic significance of stenosis
Coronary stenosis severity at CCTA; myocardial iodine uptake at baseline, rest, and stress CTP; diagnostic performance of iodine uptake values against invasive FFR.
Time frame: 36 months
Identification of geometric features from PCD-CT ultra high resolution CCTA associated to plaque-specific ischemia at invasive FFR.
Blinded analysis of baseline CCTA for qualitative and quantitative plaque characteristics, with correlation to invasive FFR. Inter-rater agreement assessment between two expert readers. Deep learning-based radiomic feature extraction from plaques and vessel walls. Development of a data-driven feature selection pipeline to predict hemodynamically significant stenosis from CCTA data, using invasive FFR as reference.
Time frame: 36 months
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