Trial reportEuropean radiology2025
Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.
Trial report in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed.
- Spectral CT in patients with colorectal cancer: quantitative contrast lesional assessment of mucinous and non-mucinous peritoneal metastases using virtual monoenergetic imaging.Abdominal radiology (New York) · 2026Article
- Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm.Insights into imaging · 2026Article
- Deep learning image reconstruction optimizes coronary artery calcium quantification.Frontiers in cardiovascular medicine · 2026Article
- Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.European radiology · 2026Article
- Comparison of DLIR and ASIR-V algorithms for virtual monoenergetic imaging in carotid CTA under a triple-low protocol.Japanese journal of radiology · 2026Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military.Bioengineering (Basel, Switzerland) · 2025Review
- Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.BMC medical imaging · 2025Article
- Innovation and Optimization of Contrast Media Administration in Computed Tomography.Korean journal of radiology · 2025Article
- Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm.Journal of clinical imaging science · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND
methodsFrom June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m
resultsThe final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001).
conclusionDLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT: Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS: Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.
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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 Socratic graph.