| ○種別 (必須): | □ | 学術論文 (審査論文)
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| ○言語 (必須): | □ | 英語
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| ○招待 (推奨): |
| ○審査 (推奨): | □ | Peer Review
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| ○カテゴリ (推奨): | □ | 研究
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| ○共著種別 (推奨): | □ | 国際共著 (徳島大学内研究者と国外研究機関所属研究者との共同研究)
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| ○学究種別 (推奨): | □ | 博士前期課程学生による研究報告
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| ○組織 (推奨): |
| ○著者 (必須): | 1. | (英) Fujiwara Daiyu (日) 藤原 大裕 (読) ふじわら だいゆう
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| ○学籍番号 (推奨): | □ | ****
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| 2. | (英) Shimomura Taisei (日) 下村 泰生 (読) しもむら たいせい
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| ○学籍番号 (推奨): | □ | ****
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| 3. | (英) Zhao Wei (日) (読)
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| 4. | (英) Li Kai-wen (日) (読)
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| 5. | 芳賀 昭弘 ([徳島大学.大学院医歯薬学研究部.保健学域.保健科学部門.放射線科学系.医用画像物理学])
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| 6. | (英) Geng Li-sheng (日) (読)
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| ○題名 (必須): | □ | (英) Virtual computed-tomography system for deep-learning-based material decomposition (日)
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| ○要約 (任意): | □ | (英) Objective: Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental information about the real human body. To overcome this problem, we developed a virtual CT system model, by which various reconstructed images can be generated based on ICRP110 human phantoms with information about six major elements (H, C, N, O, P, and Ca).Approach: We generated CT datasets labelled with accurate elemental information using the proposed generative CT model and trained a deep learning (DL)-based model to estimate the material distribution with the ICRP110 based human phantom as well as the digital SheppLogan phantom. The accuracy in quad-, dual-, and single-energy CT cases was investigated. The influence of beam-hardening artefacts, noise, and spectrum variations were analysed with testing datasets including elemental density and anatomical shape variations.Main results: The results indicated that this DL approach can realise precise MD, even with single-energy CT images. Moreover, noise, beam-hardening artefacts, and spectrum variations were shown to have minimal impact on the MD. Significance: Present results suggest that the difficulty to prepare a large CT database can be solved by introducing the virtual CT system and the proposed technique can be applied to clinical radiodiagnosis and radiotherapy. (日)
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| ○キーワード (推奨): | 1. | (英) Artifacts (日) (読)
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| 2. | (英) Deep Learning (日) (読)
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| 3. | (英) Humans (日) (読)
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| 4. | (英) Image Processing, Computer-Assisted (日) (読)
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| 5. | (英) Phantoms, Imaging (日) (読)
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| 6. | (英) Tomography, X-Ray Computed (日) (読)
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| ○発行所 (推奨): |
| ○誌名 (必須): | □ | Physics in Medicine and Biology (Hospital Physicists' Association)
(pISSN: 0031-9155, eISSN: 1361-6560)
| ○ISSN (任意): | □ | 1361-6560
ISSN: 0031-9155
(pISSN: 0031-9155, eISSN: 1361-6560) Title: Physics in medicine and biologyTitle(ISO): Phys Med BiolPublisher: Institute of Physics (NLM Catalog)
(Scopus)
(CrossRef)
(Scopus information is found. [need login]) | [継承] |
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| ○巻 (必須): | □ | 67
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| ○号 (必須): | □ | 15
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| ○頁 (必須): | □ | 155008 155008
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| ○都市 (任意): |
| ○年月日 (必須): | □ | 西暦 2022年 7月 19日 (令和 4年 7月 19日)
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| ○URL (任意): |
| ○DOI (任意): | □ | 10.1088/1361-6560/ac7bcd (→Scopusで検索)
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| ○PMID (任意): | □ | 35738247 (→Scopusで検索)
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| ○指導教員 (推奨): | 1. | 芳賀 昭弘 ([徳島大学.大学院医歯薬学研究部.保健学域.保健科学部門.放射線科学系.医用画像物理学])
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| ○備考 (任意): | 1. | (英) Article.ELocationID: 10.1088/1361-6560/ac7bcd (日)
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| 2. | (英) Article.PublicationTypeList.PublicationType: Journal Article (日)
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| 3. | (英) Article.PublicationTypeList.PublicationType: Research Support, Non-U.S. Gov't (日)
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| 4. | (英) KeywordList.Keyword: ICRP110 human phantom (日)
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| 5. | (英) KeywordList.Keyword: computed tomography (日)
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| 6. | (英) KeywordList.Keyword: deep learning (日)
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| 7. | (英) KeywordList.Keyword: material decomposition (日)
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