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