ArticleLa Radiologia medica2023
Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.European radiology · 2025Trial
- Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.Insights into imaging · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflow.Frontiers in 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
- Differences in different reconstruction algorithms for coronary CTA demonstrating pericoronary adipose tissue attenuation.Scientific reports · 2025Article
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.European radiology · 2025Article
- Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.Journal of imaging informatics in medicine · 2025Article
- Innovation and Optimization of Contrast Media Administration in Computed Tomography.Korean journal of radiology · 2025Article
- Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.La Radiologia medica · 2025Article
- Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.Journal of radiation research · 2025Review
- Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.Scientific reports · 2024Article
- Article
- Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.Tomography (Ann Arbor, Mich.) · 2023Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V). MATERIAL AND
methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.
resultsDLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021). DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4-4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).
conclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.
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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.