{"title":"深度学习重建提高超高分辨率脑CT血管造影图像质量：在烟雾病中的应用。","authors":"Yongping Ma, Satoshi Nakajima, Yasutaka Fushimi, Takeshi Funaki, Sayo Otani, Miyuki Takiya, Akira Matsuda, Satoshi Kozawa, Yasuhiro Fukushima, Sachi Okuchi, Akihiko Sakata, Takayuki Yamamoto, Ryo Sakamoto, Hideo Chihara, Yohei Mineharu, Yoshiki Arakawa, Yuji Nakamoto","doi":"10.1007/s11604-025-01806-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To 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).</p><p><strong>Materials and methods: </strong>This 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.</p><p><strong>Results: </strong>DLR-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).</p><p><strong>Conclusion: </strong>DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"1634-1642"},"PeriodicalIF":2.1000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479687/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.\",\"authors\":\"Yongping Ma, Satoshi Nakajima, Yasutaka Fushimi, Takeshi Funaki, Sayo Otani, Miyuki Takiya, Akira Matsuda, Satoshi Kozawa, Yasuhiro Fukushima, Sachi Okuchi, Akihiko Sakata, Takayuki Yamamoto, Ryo Sakamoto, Hideo Chihara, Yohei Mineharu, Yoshiki Arakawa, Yuji Nakamoto\",\"doi\":\"10.1007/s11604-025-01806-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To 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).</p><p><strong>Materials and methods: </strong>This 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.</p><p><strong>Results: </strong>DLR-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).</p><p><strong>Conclusion: </strong>DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.</p>\",\"PeriodicalId\":14691,\"journal\":{\"name\":\"Japanese Journal of Radiology\",\"volume\":\" \",\"pages\":\"1634-1642\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479687/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Japanese Journal of Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11604-025-01806-5\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese Journal of Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11604-025-01806-5","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/29 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.
Purpose: To 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 methods: This 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.
Results: DLR-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).
Conclusion: DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.
期刊介绍：
Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.
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