ArticleBMC medical imaging2023
75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Pooled it
- Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study.Diagnostics (Basel, Switzerland) · 2026Article
- Comparing the detectability of pulmonary nodules on two ultra-high resolution CT scanners: a preliminary phantom study.European radiology · 2026Article
- BMI-based sub-millisievert low-dose CT: suitable for lung cancer screening?European radiology · 2026Article
- The impact of a novel deep learning reconstruction algorithm on image quality in ultralow-dose CT: a quantitative phantom study.European radiology experimental · 2026Article
- Reframing computed tomography-associated cancer concerns in lifesaving screening: keeping the focus on lives saved.Lancet regional health. Americas · 2026Review
- Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.European radiology experimental · 2026Article
- Strategies for sustainable lung cancer screening: a multi-faceted perspective to long-term surveillance in SOLACE.Insights into imaging · 2026Article
- Comparison of deep learning reconstruction and iterative reconstruction algorithms for virtual monoenergetic image quality in overweight and obese patients with triple-low scan protocol dual-energy carotid computed tomography angiography.Quantitative imaging in medicine and surgery · 2026Article
- Prevalence and regional distribution of osteoporosis in adults aged 75 years and older: a quantitative computed tomography-based retrospective cross-sectional study.BMC geriatrics · 2026Article
- Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction: a single‑institution study.Radiological physics and technology · 2025Article
- Comprehensive linkage between molecular biology and imaging radiomics for thyroid nodules.World journal of radiology · 2025Article
- Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.Research in diagnostic and interventional imaging · 2025Article
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- Artificial Intelligence in Chest Computed Tomography Optimization: A Task-based Framework for Balancing Detectability and Radiation Dose.Journal of medical physicsReview
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveFew studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND
methodsWe retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).
resultsThe median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).
conclusionQLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.
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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 OpenQuestion graph.