Dreesen, Hendrik J. H. and Stroszczynski, Christian and Lell, M. M. (2024) Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm. JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 37 (4). pp. 1548-1556. ISSN 2948-2925, 2948-2933
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Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts. 124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA) and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman's Rho. Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 +/- 1.65 to 2.26 +/- 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction. The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MOTION-CORRECTION ALGORITHM; HIGH HEART-RATE; CT ANGIOGRAPHY; IMAGE QUALITY; DIAGNOSTIC PERFORMANCE; IMPACT; INTERPRETABILITY; SOCIETY; SCAN; Coronary computed tomography angiography; Single-source computed tomography; 64-Detector row computed tomography; Motion artifact reduction; Deep learning-based algorithm; Motion correction algorithm |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Röntgendiagnostik |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Oct 2025 05:11 |
| Last Modified: | 14 Oct 2025 05:11 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64254 |
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