Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced
Akira Katayama1, Koichiro Yasaka2, Yasunori Hamaguchi1
1Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7- 3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Abstract:
This study aimed to evaluate the image quality and delineation of the cystic artery and related abdominal vessels using Super-Resolution Deep Learning Reconstruction (SR-DLR) on contrast-enhanced computed tomography (CT), compared with standard Deep Learning Reconstruction (DLR). This retrospective study included 60 patients who underwent contrast-enhanced abdominal CT with an arterial phase between April and June 2025. Images were reconstructed using both SR-DLR and DLR algorithms. Quantitative evaluation included CT attenuation values, image noise, contrast-to-noise ratio (CNR), full width at half maximum (FWHM), and edge rise distance and edge rise slope (ERD/ERS) for the superior mesenteric artery (SMA) and proper hepatic artery (PHA). Three radiologists independently performed qualitative assessments of cystic artery, PHA, SMA, and cystic duct delineation, as well as image noise, sharpness, and artifacts. SR-DLR showed significantly lower image noise and higher CT attenuation values and CNR than DLR (p < 0.001). SR-DLR yielded higher ERS and lower ERD for the PHA and SMA compared to DLR (p ≤ 0.006). Qualitative analysis demonstrated that all three readers rated cystic artery delineation as significantly better with SR-DLR than with DLR (p ≤ 0.018). SR-DLR also achieved higher scores for image sharpness and diagnostic acceptability (p ≤ 0.001). SR-DLR significantly improves the delineation and image quality of the cystic artery and adjacent vascular anatomy compared with DLR. While no significant improvement in cystic artery detection was observed, the enhanced image quality may better support clinical confidence in preoperative planning for cholecystectomy and interventional radiology procedures.
