ArticleJapanese journal of radiology2025
Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning-Based Image Reconstruction in Ultra-High-Resolution CT Venography for Improved Visualization of Cerebral Venous Drainage.Clinical neuroradiology · 2026Article
- Imaging diagnosis of cystic intraparenchymal brain neoplasms.Japanese journal of radiology · 2026Review
- Response to Letter to the Editor: Impact of 1024-Matrix Size on Perforating Artery Visualization in Cerebral CTA Using a 64-Slice CT Scanner.Journal of medical radiation sciences · 2026Article
- Deep learning reconstruction enhances 1.5T MR angiography beyond 3T in vascular visualization for Moyamoya disease.Japanese journal of radiology · 2026Article
- Molecular and multimodal biomarkers in Moyamoya disease: from pathogenic mechanisms to clinical translation.European journal of medical research · 2026Review
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17 authors.
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Abstract
purposeTo investigate vessel delineation and image quality of ultra-high-resolution (UHR) CT angiography (CTA) reconstructed using deep learning reconstruction (DLR) optimised for brain CTA (DLR-brain) in moyamoya disease (MMD), compared with DLR optimised for body CT (DLR-body) and hybrid iterative reconstruction (Hybrid-IR). MATERIALS AND
methodsThis retrospective study included 50 patients with suspected or diagnosed MMD who underwent UHR brain CTA. All images were reconstructed using DLR-brain, DLR-body, and Hybrid-IR. Quantitative analysis focussed on moyamoya perforator vessels in the basal ganglia and periventricular anastomosis. For these small vessels, edge sharpness, peak CT number, vessel contrast, full width at half maximum (FWHM), and image noise were measured and compared. Qualitative analysis was performed by visual assessment to compare vessel delineation and image quality.
resultsDLR-brain significantly improved edge sharpness, peak CT number, vessel contrast, and FWHM, and significantly reduced image noise compared with DLR-body and Hybrid-IR (P < 0.05). DLR-brain significantly outperformed the other algorithms in the visual assessment (P < 0.001).
conclusionDLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.
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