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ID 119336
Title Alternative
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
Author
Shimomura, Taisei Tokushima University|The University of Tokyo
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
Keywords
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
Content Type
Thesis or Dissertation
Description
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.
Journal Title
Physica Medica
ISSN
1724191X
11201797
NCID
AA12796015
Publisher
Elsevier
Volume
113
Start Page
102648
Published Date
2023-09-04
Remark
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
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
ETD
MEXT report number
甲第3804号
Diploma Number
甲保第67号
Granted Date
2024-03-22
Degree Name
Doctor of Health Science
Grantor
Tokushima University
departments
Medical Sciences