Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging

Dynamic CT Myocardial Perfusion: The Role of Functional Evaluation in the Diagnosis of Coronary Artery Disease

Coronary computed tomography angiography (CTA) is a widely accepted, non-invasive diagnostic modality for the evaluation of patients with suspected coronary artery disease (CAD). However, a limitation of CTA is its inability to provide information on the hemodynamic significance of the coronary lesion. The recently developed stress dynamic CT perfusion technique has emerged as a potential solution to this diagnostic challenge. Dynamic CT myocardial perfusion provides information on the hemodynamic consequences of coronary stenosis and is used to detect myocardial ischemia. The combination of stress dynamic CT myocardial perfusion with CTA provides a comprehensive assessment that integrates anatomical and functional information. CT myocardial perfusion has been validated in several clinical studies and has shown comparable accuracy to Positron Emission Tomography (PET) and stress magnetic resonance imaging (MRI) in the diagnosis of hemodynamically significant coronary stenosis and superior performance to Single Photon Emission Computed Tomography (SPECT). More importantly, CTP-derived myocardial perfusion has been shown to have a strong correlation with FFR, and the use of CTP results in a reduction of negative catheterizations. In the context of suspected stable coronary artery disease, the CT protocol with dynamic perfusion imaging combined with CTA eliminates the need for additional testing, making it a convenient "one-stop-shop" method and an effective gatekeeper to an invasive approach.

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.