ArticleEuropean radiology2023
Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in personalized computed tomography dose optimization: a systematic review of techniques and clinical outcomes.Frontiers in medicine · 2026Pooled it
- Pooled it
- Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.European radiology · 2024Trial
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Deep learning‑based ultra-high-resolution CT imaging of viral pneumonia at admission and after discharge.BMC medical imaging · 2026Article
- Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on automated Alberta stroke program early CT score- evaluation.Neuroradiology · 2026Article
- Diagnostic value of a second-generation super-resolution deep learning-based reconstruction combined with a metal artifact reduction algorithm for pelvic CT.Skeletal radiology · 2026Article
- Deep learning image reconstruction technique for improving image quality and radiation dose reduction compared to iterative reconstruction technique in non-contrast CT head imaging.Frontiers in radiology · 2026Article
- Optimizing infant magnetic resonance imaging efficiency: Deep learning-assisted feed-and-wrap technique versus general anesthesia using an infant magnetic resonance imaging stabilizer in infants under 4 months.Pediatric radiology · 2026Article
- Deep learning-based reconstruction improves image quality in low-dose head CT angiography.Malawi medical journal : the journal of Medical Association of Malawi · 2025Article
- Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.BMC medical imaging · 2025Article
- Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.Tomography (Ann Arbor, Mich.) · 2024Article
- Assessment of vascular invasion of pancreatic ductal adenocarcinoma based on CE-boost black blood CT technique.Insights into imaging · 2024Article
- Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.Journal of applied clinical medical physics · 2024Article
- Performance of Iterative Reconstruction in Image Space Algorithm in Combination with Automatic Tube Current Modulation Compared to Filtered Back Projection in Brain CT Scan.Journal of biomedical physics & engineering · 2024Article
- Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography.Neuroradiology · 2024Article
- The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study.Journal of imaging · 2023Article
- Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.Tomography (Ann Arbor, Mich.) · 2023Article
- Dependence of Resolution Properties on Display Field-of-view Size in Deep Learning-based Reconstruction Computed Tomography Images for Peripheral Vascular Imaging.Journal of medical physicsArticle
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18 authors.
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Abstract
objectivesTo evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images.
methodsThis retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured.
resultsThe effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively.
conclusionDLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS: • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.
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