Affiliation:
1. Department of Radiology, Iwate Medical University
2. Center for Radiological Science, Iwate Medical University
Abstract
Abstract
The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between super-resolution deep learning reconstruction (SR-DLR) and model-based iterative reconstruction (MBIR). We prospectively enrolled 50 patients (median age, 68 years; interquartile range [IQR], 59–74 years; 34 men) who underwent coronary computed tomography angiography (CCTA) using a 320-detector row CT scanner between January and April 2022. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the proximal coronary arteries were calculated. Of the ten stents, stent strut thickness and luminal diameter were quantitatively evaluated. The image noise on SR-DLR was significantly lower than that on MBIR (median 22.1 HU; IQR, 19.1–24.5 HU vs. 27.4 HU; IQR, 24.1–31.1 HU, p < 0.01), whereas the SNR (median 16.3; IQR, 12.0–22.0 vs. 13.9; IQR, 9.8–19.2, p = 0.03) and CNR (median 25.2; IQR, 16.9–30.8 vs. 19.5; IQR, 14.5–23.7, p < 0.01) on SR-DLR were significantly higher than that on MBIR. Stent struts were significantly thinner (median, 0.66 mm; IQR, 0.61–0.72 mm vs. 0.80 mm; IQR, 0.68–0.86 mm, p < 0.01) and in-stent lumens were significantly larger (median, 1.82 mm; IQR, 1.57–1.95 mm vs. 1.34 mm; IQR, 1.26–1.60 mm, p < 0.01) on SR-DLR than on MBIR. This study’s initial experience with SR-DLR improves the image quality of the coronary arteries and in-stent lumen at CCTA compared with conventional MBIR.
Publisher
Research Square Platform LLC