ArticleNeuroradiology2024
Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography.
Article in Neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port.Journal of imaging informatics in medicine · 2026Article
- Comparison of deep learning reconstruction and adaptive statistical iterative reconstruction for head CT in acute stroke.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 reconstruction for noncontrast head computed tomography: Improved image quality and potential diagnostic impact in acute ischemic stroke.Neuroradiology · 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
- Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.Japanese journal of radiology · 2025Article
- Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis.Emergency radiology · 2025Article
- Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.Tomography (Ann Arbor, Mich.) · 2024Article
- Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.Journal of imaging informatics in medicine · 2024Article
- Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction.Neuroradiology · 2024Article
- Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.Journal of neuro-oncology · 2024Article
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6 authors.
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Abstract
purposeThis study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR).
methodsThis retrospective study included 29 (75.8 ± 13.2 years, 20 males) and 26 (64.4 ± 12.4 years, 18 males) patients with and without acute infarction, respectively. Unenhanced head CT images were reconstructed with DLR and Hybrid IR. In qualitative analyses, three readers evaluated the conspicuity of lesions based on five regions and image quality. A radiologist placed regions of interest on the lateral ventricle, putamen, and white matter in quantitative analyses, and the standard deviation of CT attenuation (i.e., quantitative image noise) was recorded.
resultsConspicuity of acute infarct in DLR was superior to that in Hybrid IR, and a statistically significant difference was observed for two readers (p ≤ 0.038). Conspicuity of acute infarct with time from onset to CT imaging at < 24 h in DLR was significantly improved compared with Hybrid IR for all readers (p ≤ 0.020). Image noise in DLR was significantly reduced compared with Hybrid IR with both the qualitative and quantitative analyses (p < 0.001 for all).
conclusionDLR in head CT helped improve acute infarct depiction, especially those with time from onset to CT imaging at < 24 h.
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