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CARDIAC IMAGINGC oronary CT angiography (CCTA) is currently recommended for the assessment of many cardiovascular diseases, including coronary artery disease (CAD) evaluation (1). CCTA is particularly important for its high negative predictive value for CAD in a low-and intermediaterisk acute chest pain population, with a high sensitivity and specificity for CAD in a low-and intermediate-risk chronic coronary syndrome population (2-5). This had been made possible by the recent technical evolution of the CT systems and the existence of large-scale validation cohort studies (6,7). However, conventional CCTA still has a limited spatial resolution and soft-tissue contrast, which impairs its diagnostic performance for small arteries (ie, ,2 mm) and high-contrast (eg, stent, calcification) and low-contrast (eg, noncalcified plaque) tasks, and carries the risks of relatively high x-ray dose delivery.Over the past 5 years, photon-counting CT (PCCT) technology has emerged in the field of CT imaging. Compared with conventional CT, this new modality has better spatial resolution and soft-tissue contrast and reduced noise, blooming, and beam-hardening artifacts (8). This is because of new energy-resolving detectors, called photon-counting Background. Spatial resolution, soft-tissue contrast, and dose-efficient capabilities of photon-counting CT (PCCT) potentially allow a better quality and diagnostic confidence of coronary CT angiography (CCTA) in comparison to conventional CT. Purpose:To compare the quality of CCTA scans obtained with a clinical prototype PCCT system and an energy-integrating detector (EID) dual-layer CT (DLCT) system. Materials and Methods:In this prospective board-approved study with informed consent, participants with coronary artery disease underwent retrospective electrocardiographically gated CCTA with both systems after injection of 65-75 mL of 400 mg/mL iodinated contrast agent at 5 mL/sec. A prior phantom task-based quality assessment of the detectability index of coronary lesions was performed. Ultra-high-resolution parameters were used for PCCT (1024 matrix, 0.25-mm section thickness) and EID DLCT (512 matrix, 0.67-mm section thickness). Three cardiac radiologists independently performed a blinded analysis using a five-point quality score (1 = insufficient, 5 = excellent) for overall image quality, diagnostic confidence, and diagnostic quality of calcifications, stents, and noncalcified plaques. A logistic regression model, adjusted for radiologists, was used to evaluate the proportion of improvement in scores with the best method.Results: Fourteen consecutive participants (12 men; mean age, 61 years 6 17) were enrolled. Scores of overall quality and diagnostic confidence were higher with PCCT images with a median of 5 (interquartile range [IQR], 2) and 5 (IQR, 1) versus 4 (IQR, 1) and 4 (IQR, 3) with EID DLCT images, using a mean tube current of 255 mAs 6 0 versus 349 mAs 6 111 for EID DLCT images (P , .01). Proportions of improvement with PCCT images for quality of calcification, stent, and non...

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Purpose To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm. Material and methods Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDIvol: 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered‐back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task‐based transfer function (TTF) for bone (high‐contrast insert) and acrylic (low‐contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (fav) of the NPS curve were measured. The detectability index (d’) was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries. Results The noise magnitude was lower with both AiCE versions than with AIDR 3De. The noise magnitude was lower with AiCE V10 than with AiCE V8 (‐4 ± 6% for Mild, ‐13 ± 3% for Standard, and ‐48 ± 0% for Strong levels). fav and TTF50% values for both inserts shifted towards higher frequencies with AiCE than with AIDR 3De. Compared to AiCE V08, fav shifted towards higher frequencies with AiCE V10 (45 ± 4%, 36 ± 3%, and 5 ± 4% for all levels, respectively). The TTF50% values shifted towards higher frequencies with AiCE V10 as compared with AiCE V8 for both inserts, except for the Strong level for the acrylic insert. Whatever the dose and AiCE levels, d’ values were higher with AiCE V10 than with AiCE V8 for the small object/calcification and for the large object/lesion. Conclusion As compared to AIDR 3De, lower noise magnitude and higher spatial resolution and detectability index were found with both versions of AiCE. As compared to AiCE V8, AiCE V10 reduced noise and improved spatial resolution and detectability without changing the noise texture in a simple geometric phantom, except for the Strong level. AiCE V10 seems to have a greater potential for dose reduction than AiCE V8.