Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction

Fuminari Tatsugami; Toru Higaki; Ikuo Kawashita; Wataru Fukumoto; Yuko Nakamura; Masakazu Matsuura; Tzu-Cheng Lee; Jian Zhou; Liang Cai; Toshiro Kitagawa; Yukiko Nakano; Kazuo Awai

doi:10.1016/j.acra.2022.12.044

Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction

Acad Radiol. 2023 Nov;30(11):2497-2504. doi: 10.1016/j.acra.2022.12.044. Epub 2023 Jan 19.

Authors

Affiliations

¹ Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima, 734-8551, Japan. Electronic address: sa104@rg8.so-net.ne.jp.
² Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima City, Hiroshima, Japan.
³ Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City, Hiroshima, 734-8551, Japan.
⁴ Canon Medical Systems Corporation, Otawara City, Tochigi, Japan.
⁵ Canon Medical Research USA, Vernon Hills, Illinois.
⁶ Department of Cardiovascular Medicine, Hiroshima University, Hiroshima City, Hiroshima, Japan.

PMID: 36681533
DOI: 10.1016/j.acra.2022.12.044

Abstract

Rationale and objectives: Our objective was to compare the image quality of coronary CT angiography reconstructed with super-resolution deep learning reconstruction (SR-DLR) and with hybrid iterative reconstruction (IR) images.

Materials and methods: This retrospective study included 100 patients who underwent coronary CT angiography using a 320-detector-row CT scanner. The CT images were reconstructed with hybrid IR and SR-DLR. The standard deviation of the CT number was recorded and the CT attenuation profile across the left main coronary artery was generated to calculate the contrast-to-noise ratio (CNR) and measure the edge rise slope (ERS). Overall image quality was evaluated and plaque detectability was assessed on a 4-point scale (1 = poor, 4 = excellent). For reference, invasive coronary angiography of 14 patients was used.

Results: The mean image noise on SR-DLR was significantly lower than on hybrid IR images (15.6 vs 22.9 HU; p < 0.01). The mean CNR was significantly higher and the ERS was steeper on SR-DLR- compared to hybrid IR images (CNR: 32.4 vs 20.4, p < 0.01; ERS: 300.0 vs 198.2 HU/mm, p < 0.01). The image quality score was better on SR-DLR- than on hybrid IR images (3.6 vs 3.1; p < 0.01). SR-DLR increased the detectability of plaques with < 50% stenosis (p < 0.01).

Conclusion: SR-DLR was superior to hybrid IR with respect to the image noise, the sharpness of coronary artery margins, and plaque detectability.

Keywords: Artificial intelligence; Coronary computed tomography angiography; Deep learning reconstruction; Super resolution.