ID | 119336 |
タイトル別表記 | 人体ファントムライブラリに基づく仮想コーンビームCTの開発と元素分布分析への応用
|
著者 |
下村, 泰生
徳島大学大学院保健科学研究科(保健学専攻)
Fujiwara, Daiyu
Tokushima University
Inoue, Yuki
Tokushima University
Takeya, Atsushi
Tokushima University
Ohta, Takeshi
The University of Tokyo
Nozawa, Yuki
The University of Tokyo
Imae, Toshikazu
The University of Tokyo
Nawa, Kanabu
The University of Tokyo
Nakagawa, Keiichi
The University of Tokyo
|
キーワード | Monte Carlo simulation
Cone-beam computed tomography
Human phantom
Material decomposition
Artificial intelligence
Computer vision
コーンビームCT
人体ファントム
元素分析
人工知能
コンピュータービジョン
|
資料タイプ |
学位論文
|
抄録 | Purpose: The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator.
Methods: The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient. Results: The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable. Conclusions: This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy. |
掲載誌名 |
Physica Medica
|
ISSN | 1724191X
11201797
|
cat書誌ID | AA12796015
|
出版者 | Elsevier
|
巻 | 113
|
開始ページ | 102648
|
発行日 | 2023-09-04
|
備考 | 内容要旨・審査要旨・論文本文の公開
本論文は,著者Taisei Shimomuraの学位論文として提出され,学位審査・授与の対象となっている。 |
EDB ID | |
出版社版DOI | |
出版社版URL | |
フルテキストファイル | |
言語 |
eng
|
著者版フラグ |
博士論文全文を含む
|
文科省報告番号 | 甲第3804号
|
学位記番号 | 甲保第67号
|
学位授与年月日 | 2024-03-22
|
学位名 |
博士(保健学)
|
学位授与機関 |
徳島大学
|
部局 |
医学系
|