ArticleRadiological physics and technology2026
Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.
Article in Radiological physics and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.