Background Recently introduced photon-counting CT may improve noninvasive assessment of patients with high risk for coronary artery disease (CAD). Purpose To determine the diagnostic accuracy of ultrahigh-resolution (UHR) coronary CT angiography (CCTA) in the detection of CAD compared with the reference standard of invasive coronary angiography (ICA). Materials and Methods In this prospective study, participants with severe aortic valve stenosis and clinically indicated CT for transcatheter aortic valve replacement planning were consecutively enrolled from August 2022 to February 2023. All participants were examined with a dual-source photon-counting CT scanner using a retrospective electrocardiography-gated contrast-enhanced UHR scanning protocol (tube voltage, 120 or 140 kV; collimation, 120 × 0.2 mm; 100 mL of iopromid; no spectral information). Subjects underwent ICA as part of their clinical routine. A consensus assessment of image quality (five-point Likert scale: 1 = excellent [absence of artifacts], 5 = nondiagnostic [severe artifacts]) and a blinded independent reading for the presence of CAD (stenosis ≥50%) were performed. UHR CCTA was compared with ICA using area under the receiver operating characteristic curve (AUC). Results Among 68 participants (mean age, 81 years ± 7 [SD]; 32 male, 36 female), the prevalence of CAD and prior stent placement was 35% and 22%, respectively. The overall image quality was excellent (median score, 1.5 [IQR, 1.3-2.0]). The AUC of UHR CCTA in the detection of CAD was 0.93 per participant (95% CI: 0.86, 0.99), 0.94 per vessel (95% CI: 0.91, 0.98), and 0.92 per segment (95% CI: 0.87, 0.97). Sensitivity, specificity, and accuracy, respectively, were 96%, 84%, and 88% per participant (n = 68); 89%, 91%, and 91% per vessel (n = 204); and 77%, 95%, and 95% per segment (n = 965). Conclusion UHR photon-counting CCTA provided high diagnostic accuracy in the detection of CAD in a high-risk population, including subjects with severe coronary calcification or prior stent placement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Williams and Newby in this issue.

Objectives: To determine the diagnostic accuracy of ultra-high-resolution photon-counting detector CT angiography (UHR PCD-CTA) for evaluating coronary stent patency compared to invasive coronary angiography (ICA). Methods: Consecutive, clinically referred patients with prior coronary stent implantation were prospectively enrolled between August 2022 and March 2023 and underwent UHR PCD-CTA (collimation, 120 × 0.2 mm). Two radiologists independently analyzed image quality of the in-stent lumen using a 5-point Likert scale, ranging from 1 (“excellent”) to 5 (“non-diagnostic”), and assessed all coronary stents for the presence of in-stent stenosis (≥ 50% lumen narrowing). The diagnostic accuracy of UHR PCD-CTA was determined, with ICA serving as the standard of reference. Results: A total of 44 coronary stents in 18 participants (mean age, 83 years ± 6 [standard deviation]; 12 women) were included in the analysis. In 3/44 stents, both readers described image quality as non-diagnostic, whereas reader 2 noted a fourth stent to have non-diagnostic image quality. In comparison to ICA, UHR PCD-CTA demonstrated a sensitivity, specificity, and accuracy of 100% (95% CI [confidence interval] 47.8, 100), 92.3% (95% CI 79.1, 98.4), and 93.2% (95% CI 81.3, 98.6) for reader 1 and 100% (95% CI 47.8, 100), 87.2% (95% CI 72.6, 95.7), and 88.6% (95% CI 75.4, 96.2) for reader 2, respectively. Both readers observed a 100% negative predictive value (36/36 stents and 34/34 stents). Stent patency inter-reader agreement was 90.1%, corresponding to a substantial Cohen’s kappa value of 0.72. Conclusions: UHR PCD-CTA enables non-invasive assessment of coronary stent patency with high image quality and diagnostic accuracy. Clinical relevance statement: Ultra-high-resolution photon-counting detector CT angiography represents a reliable and non-invasive method for assessing coronary stent patency. Its high negative predictive value makes it a promising alternative over invasive coronary angiography for the rule-out of in-stent stenosis. Key Points: • CT-based evaluation of coronary stent patency is limited by stent-induced artifacts and spatial resolution. • Ultra-high-resolution photon-counting detector CT accurately evaluates coronary stent patency compared to invasive coronary angiography. • Photon-counting detector CT represents a promising method for the non-invasive rule-out of in-stent stenosis.

The aim of this paper is to evaluate the diagnostic image quality of spectral dual-source photon-counting detector coronary computed tomography angiography (PCD-CCTA) for coronary artery disease in a multicenter study. The image quality (IQ), assessability, contrast-to-noise ratio (CNR), Agatston score, and radiation exposure were measured. Stenoses were quantified and compared with invasive coronary angiography, if available. A total of 92 subjects (65% male, age 58 ± 14 years) were analyzed. The prevalence of significant coronary artery disease (CAD) (stenosis ≥ 50%) was 17% of all patients, the range of the Agatston score was 0–2965 (interquartile range (IQR) 0–135). The IQ was very good (one, IQR one–two), the CNR was very high (20 ± 10), and 5% of the segments were rated non-diagnostic. The IQ and assessability were higher in proximal coronary segments (p < 0.001). Agatston scores up to 600 did not significantly affect the assessability of the coronary segments (p = 0.3). Heart rate influenced assessability only at a high-pitch mode (p = 0.009). For the invasive coronary angiography (ICA) subgroup (n = nine), the diagnostic performance for CAD per segment was high (sensitivity 92%, specificity 96%), although the limited number of patients who underwent both diagnostic modalities limits the generalization of this finding at this stage. PCD-CCTA provides good image quality for low and moderate levels of coronary calcifications.

There was no difference in diagnostic accuracy for detecting hemodynamically relevant coronary stenosis with CT myocardial perfusion versus CT-derived fractional flow reserve.

The Coronary Artery Disease-Reporting and Data System (CAD-RADS) 2.0 offers standardized guidelines for interpreting coronary artery disease in cardiac CT. Accurate and consistent CAD-RADS 2.0 scoring is crucial for comprehensive disease characterization and clinical decision-making. This study investigates the capability of large language models (LLMs) to autonomously generate CAD-RADS 2.0 scores from cardiac CT reports. A dataset of cardiac CT reports was created to evaluate the performance of several state-of-the-art LLMs in generating CAD-RADS 2.0 scores via in-context learning. The tested models comprised GPT-3.5, GPT-4o, Mistral 7b, Mixtral 8 ​× ​7b, Llama3 8b, Llama3 8b with a 64k context length, and Llama3 70b. The generated scores from each model were compared to the ground truth, which was provided by two board-certified cardiothoracic radiologists in consensus based on the reports. The final set comprised 200 cardiac CT reports. GPT-4o and Llama3 70b achieved the highest accuracy in generating full CAD-RADS 2.0 scores including all modifiers with a performance rate of 93 ​% and 92.5 ​%, respectively, followed by Mixtral 8 ​× ​7b with 78 ​%. In contrast, older LLMs, such as Mistral 7b and GPT-3.5 performed poorly (16 ​%) and Llama3 8b demonstrated intermediate results with an accuracy of 41.5 ​%. LLMs enhanced with in-context learning are capable of autonomously generating CAD-RADS 2.0 scores for cardiac CT reports with excellent accuracy, potentially enhancing both the efficiency and consistency of cardiac CT reporting. Open-source models not only deliver competitive accuracy but also present the benefit of local hosting, mitigating concerns around data security.