ArticleNeuroradiology2021
Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).
Article in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Pooled it
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Performance of Deep Learning Reconstruction for Detection of Early Ischemic Changes in NCCT: Comparison with ASIR-V in Acute Stroke.Clinical neuroradiology · 2026Article
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Brain cone-beam CT image quality improvement using a deep-learning-based denoising method: a multicenter retrospective study.Neuroradiology · 2026Article
- Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.European radiology · 2026Article
- Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.European journal of radiology open · 2026Article
- Deep-learning reconstruction for noncontrast head computed tomography: Improved image quality and potential diagnostic impact in acute ischemic stroke.Neuroradiology · 2026Article
- Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on automated Alberta stroke program early CT score- evaluation.Neuroradiology · 2026Article
- Comparison of deep learning reconstruction and adaptive statistical iterative reconstruction for head CT in acute stroke.Neuroradiology · 2026Article
- Effect of deep learning reconstruction on arm-induced artifacts compared with hybrid iterative reconstruction and filtered-backprojection in abdominal CT.Radiological physics and technology · 2026Article
- Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.Journal of imaging informatics in medicine · 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
- Optimization of image quality and diagnostic accuracy for vascular stenosis in computed tomography perfusion-reconstructed cerebral computed tomography angiography using deep learning reconstruction.Journal of clinical imaging science · 2026Article
- Quantitative and qualitative assessment of ultra-low-dose paranasal sinus CT using deep learning image reconstruction: a comparison with hybrid iterative reconstruction.European radiology · 2025Article
- Thin-Slice Brain CT Image Quality and Lesion Detection Evaluation in Deep Learning Reconstruction Algorithm.Clinical neuroradiology · 2025Article
- Article
- Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography.BMC medical imaging · 2025Article
- Improved CTA imaging for stroke evaluation - deep learning and iterative reconstruction comparative study.Neuroradiology · 2025Article
- Ultra-low-dose hepatic computed tomography with a novel real-time deep learning-based noise reduction algorithm: a prospective cross-sectional analysis of image quality and lesion detection.Quantitative imaging in medicine and surgery · 2025Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
methodsSixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.
resultsThere was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.
conclusionOn routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.
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