Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm

• Published in: Journal of digital imaging Vol. 37; no. 4; pp. 1548 - 1556
• Main Authors: Dreesen, H. J. H., Stroszczynski, C., Lell, M. M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024; Springer Nature B.V
• Subjects: Aged; Algorithms; Angiography; Computed tomography; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary artery; Coronary Vessels - diagnostic imaging; Correlation; Deep Learning; Diagnosis; Female; Heart rate; Humans; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Machine learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Multidetector Computed Tomography - methods; Radiology; Rank tests; Segments; Temporal resolution; Tomography; Veins & arteries; Deep learning-based algorithm; 64-Detector row computed tomography; Coronary computed tomography angiography; Motion correction algorithm; Single-source computed tomography; Motion artifact reduction
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01033-w

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.