High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard

Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence

In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI-driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluations and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions. Coronary artery calcium score (CACS) and CT angiography (CTA) provide high-resolution images that facilitate the detailed anatomical evaluation of coronary plaque burden. New tools such as myocardial CT perfusion (CTP) and fractional flow reserve (FFRCT) have been developed to add a functional evaluation of the stenosis. Moreover, epicardial adipose tissue (EAT) is gaining interest as its role in coronary artery plaque development has been deepened. Seen the great added value of these tools, the demand for new exams has increased such as the burden on imagers. Due to its ability to fast compute multiple data, AI can be helpful in both the acquisition and post-processing phases. AI can possibly reduce radiation dose, increase image quality, and shorten image analysis time. Moreover, different types of data can be used for risk assessment and patient risk stratification. Recently, the focus of the scientific community on AI has led to numerous studies, especially on CACS and CTA. This narrative review concentrates on AI's role in the post-processing of CACS, CTA, FFRCT, CTP, and EAT, discussing both current capabilities and future directions in the field of cardiac imaging.

Current Status of Cardiac CT for Nuclear Medicine Professionals: Coronary Artery Disease Evaluation

With advances in computed tomography (CT) technology over the past two decades, cardiac CT has become a noninvasive diagnostic tool for morphological evaluation of coronary artery disease (CAD) caused by atherosclerotic plaques and stenosis and serves as a "gatekeeper" before invasive coronary angiography. Additionally, cardiac CT stress perfusion and CT-derived fractional flow reserve can be used to assess the hemodynamic significance of coronary artery stenosis. Delayed enhancement CT can detect and localize myocardial infarction and assess myocardial viability. Currently, cardiac CT serves as a potential "one-stop-shop" imaging modality for the comprehensive assessment of patients with suspected or known CAD by providing analysis of coronary anatomy, functional significance, and characterization of left ventricular myocardium in a single session. It is crucial for nuclear medicine professionals to be aware of the current capability of cardiac CT and its ability to perform comprehensive and accurate nuclear cardiac imaging studies, which are essential for functional assessment of CAD.

Scarpa Matuck B, Yang W, Qin L, Yan F, Lima JAC, Arbab-Zadeh A, Wolfe S. Prognostic Value of Coronary CT Angiography-Derived Quantitative Flow Ratio in Suspected Coronary Artery Disease

Background The prognostic value of coronary CT angiography (CTA)-derived quantitative flow ratio (CT-QFR) remains unknown. Purpose To determine the prognostic value of CT-QFR in predicting the long-term outcomes of patients with suspected coronary artery disease (CAD) in comparison with invasive coronary angiography (ICA)/SPECT and to determine the influence of prior percutaneous coronary intervention (PCI) on the prognostic value of CT-QFR. Materials and Methods In this secondary analysis of the prospective international CORE320 study, 379 participants who underwent coronary CTA and SPECT within 60 days before ICA between November 2009 and July 2011 were included for follow-up. The coronary CTA images were analyzed to determine CT-QFR. The primary outcome was major adverse cardiovascular events (MACEs) in the 5-year follow-up. Kaplan-Meier curves, multivariable Cox regression models adjusted for clinical variables, and areas under the receiver operating characteristic curves (AUCs) were used to assess and compare the predictive ability of CT-QFR and ICA/SPECT. Results CT-QFR computation and 5-year follow-up data were available for 310 participants (median age, 62 years), of whom 205 (66%) were male. CT-QFR (hazard ratio, 1.9 [95% CI: 1.0, 3.5]; P = .04) and prior myocardial infarction (hazard ratio, 2.5 [95% CI: 1.5, 4.0]; P < .001) were independent predictors of MACE occurrence in the 5-year follow-up. MACE-free survival rates were similar in participants with normal CT-QFR and ICA/SPECT (82% vs 80%; P = .45) and in participants with abnormal CT-QFR and ICA/SPECT findings (60% vs 57%; P = .40). In participants with prior PCI, CT-QFR had a lower AUC in predicting MACEs than in participants without prior PCI (0.44 vs 0.70; P < .001). Conclusion CT-QFR was an independent predictor of MACEs in the 5-year follow-up in participants with suspected CAD and showed similar 5-year prognostic value to ICA/SPECT; however, prior PCI affected CT-QFR ability to predict MACEs. Clinical trial registration no. NCT00934037 © RSNA, 2024 Supplemental material is available for this article.

Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement

Objectives: CT-derived fractional flow reserve (CT-FFR) can improve the specificity of coronary CT-angiography (cCTA) for ruling out relevant coronary artery disease (CAD) prior to transcatheter aortic valve replacement (TAVR). However, little is known about the reproducibility of CT-FFR and the influence of diffuse coronary artery calcifications or segment location. The objective was to assess the reliability of machine-learning (ML)-based CT-FFR prior to TAVR in patients without obstructive CAD and to assess the influence of image quality, coronary artery calcium score (CAC), and the location of measurement within the coronary tree. Methods: Patients assessed for TAVR, without obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers with differing experience. Differences in absolute values and categorization into hemodynamically relevant CAD (CT-FFR ≤ 0.80) were compared. Results in regard to CAD were also compared against invasive coronary angiography. The influence of segment location, image quality, and CAC was evaluated. Results: Of the screened patients, 109/388 patients did not have obstructive CAD on cCTA and were included. The median (interquartile range) difference of CT-FFR values was -0.005 (-0.09 to 0.04) (p = 0.47). Differences were smaller with high values. Recategorizations were more frequent in distal segments. Diagnostic accuracy of CT-FFR between both observers was comparable (proximal: Δ0.2%; distal: Δ0.5%) but was lower in distal segments (proximal: 98.9%/99.1%; distal: 81.1%/81.6%). Image quality and CAC had no clinically relevant influence on CT-FFR. Conclusions: ML-based CT-FFR evaluation of proximal segments was more reliable. Distal segments with CT-FFR values close to the given threshold were prone to recategorization, even if absolute differences between observers were minimal and independent of image quality or CAC.

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.