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.

Background: Acute complete occlusion of a coronary artery results in progressive ischemia, moving from the endocardium to the epicardium (ie, wavefront). Dependent on time to reperfusion and collateral flow, myocardial infarction ( MI ) will manifest, with transmural MI portending poor prognosis. Late gadolinium enhancement cardiac magnetic resonance imaging can detect MI with high diagnostic accuracy. Primary percutaneous coronary intervention is the preferred reperfusion strategy in patients with ST ‐segment–elevation MI with <12 hours of symptom onset. We sought to visualize time‐dependent necrosis in a population with ST ‐segment–elevation MI by using late gadolinium enhancement cardiac magnetic resonance imaging (STEMI‐SCAR project). Methods and Results: ST ‐segment–elevation MI patients with single‐vessel disease, complete occlusion with TIMI (Thrombolysis in Myocardial Infarction) score 0, absence of collateral flow (Rentrop score 0), and symptom onset 121 minutes demonstrated increased infarct size, transmurality index, and transmurality grade (all P <0.01) and decreased myocardial salvage index ( P <0.001) versus patients with symptom‐to‐balloon times ≤121 minutes. Conclusions: In MI with no residual antegrade and no collateral flow, immediate reperfusion is vital. A symptom‐to‐balloon time of >121 minutes causes a high grade of transmural necrosis. In this pure ST ‐segment–elevation MI population, time to reperfusion to salvage myocardium was less than suggested by current guidelines.

To determine morphological and functional cardiovascular magnetic resonance (CMR) patterns in histopathologically confirmed myocardial involvement in patients with systemic sclerosis (SSc). Twenty patients (6 females; mean age 41 ± 11 years) with histopathologically proven cardiac involvement in SSc in the years 2008–2016 were retrospectively evaluated. Morphological, functional and late gadolinium enhancement (LGE) images were acquired in standard angulations at 1.5 T CMR. Pathologies were categorized: 1) Pericardial effusion; 2) pathologic left (LV) or right ventricular (RV) contractility (hypokinesia, dyssynchrony, and diastolic restriction); 3) reduced left (LV-EF) and right ventricular ejection fraction (RV-EF); 4) fibrosis and/or inflammation (positive LGE); 5) RV dilatation. 95 % confidence intervals (CI) were calculated for appearance of pathologic EF and RV dilatation. Seven patients (35 %) had positive CMR findings in three categories, 9 patients (45 %) in four categories and 4 patients (20 %) in five categories. The distribution of pathologic findings was: minimal pericardial effusion in 7 patients (35 %), moderate pericardial effusion >5 mm in nine patients (45 %); abnormal LV or RV contractility in 19 patients (95 %), reduced LV or RV function in 14 patients (70 %; 95 % CI: 51–88 %), pathologic LGE in all patients, RV dilatation in 6 patients (30 %; 95 % CI: 15–54 %). CMR diagnosis of myocardial involvement in SSc requires increased attention to subtle findings. Pathologic findings in at least three of five categories indicate myocardial involvement in SSc.

The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level. Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.