1. Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience
- Author
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Makoto Orii, Misato Sone, Takeshi Osaki, Yuta Ueyama, Takuya Chiba, Tadashi Sasaki, and Kunihiro Yoshioka
- Subjects
Coronary computed tomography angiography ,Super-resolution deep learning reconstruction ,Model-based iterative reconstruction ,Signal-to-noise ratio ,Contrast-to-noise ratio ,Medical technology ,R855-855.5 - Abstract
Abstract A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector row CT scanner. However, the advantages of SR-DLR at coronary computed tomography angiography (CCTA) have not been fully investigated. The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between SR-DLR and model-based iterative reconstruction (MBIR). We prospectively enrolled 70 patients (median age, 69 years; interquartile range [IQR], 59–75 years; 50 men) who underwent CCTA using a 320-detector row CT scanner between January and August 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 twenty 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, 19.3–24.9 HU vs. 27.4 HU; IQR, 24.2–31.2 HU, p
- Published
- 2023
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