ArticleQuantitative imaging in medicine and surgery2025
The application value of deep learning-based reconstruction combined with segmented contrast injection in "one-stop" cardio-cerebrovascular computed tomography angiography.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Quantification of plaque and detection of high-risk plaque characteristics based on coronary computed tomography angiography: comparison between superresolution deep learning reconstruction and hybrid iterative reconstruction.Quantitative imaging in medicine and surgery · 2026Article
- Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.BMC medical imaging · 2026Article
- A wide-detector CT protocol for one-stop cardio-cerebrovascular angiography: a technical feasibility study comparing an integrated protocol with conventional two-scan approach.Frontiers in radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: "One-stop" cardio-cerebrovascular computed tomography angiography (C&C-CTA) has become a current mainstay of craniocervical and coronary arteries screening, but concerns remain regarding the radiation safety and contrast nephropathy issues associated with CTA examinations. This study aimed to investigate the feasibility of reducing radiation dose, contrast dose, and injection rate using C&C-CTA based on deep learning image reconstruction (DLIR) algorithms. Methods: A total of 150 patients were prospectively enrolled and divided into 3 groups (A, B, and C) with 50 patients in each group. In group A, C&C-CTA was performed with a tube voltage of 80 kVp, a contrast injection protocol with segmented contrast injection, and high-strength DLIR. In groups B and C, craniocervical and coronary CTA were performed with 100 kVp and 50% adaptive statistical iterative reconstruction-V (ASIR-V), respectively. Radiation dose, contrast dose, injection rate, and subjective and objective image quality were quantified and compared. Results: Compared with group B, group A showed higher computed tomography (CT) Hounsfield unit (HU) values for the craniocervical arteries (all P<0.001). In contrast, when compared with group C, group A had lower CT HU values for the coronary artery roots (P=0.02), yet no statistically significant difference was observed in the values for other vessels (both P>0.05). The signal-to-noise ratios (SNRs) were not statistically different in vertebral artery, internal carotid artery (ICA) C4 segment (A Conclusions: Compared to scanning the coronary and craniocervical artery individually, C&C-CTA based on DLIR can reduce the radiation dose, contrast dose, and injection rate without compromising image quality.
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.