Your browser doesn't support javascript.
loading
Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.
Chang, Chih-Wei; Zhou, Shuang; Gao, Yuan; Lin, Liyong; Liu, Tian; Bradley, Jeffrey D; Zhang, Tiezhi; Zhou, Jun; Yang, Xiaofeng.
Afiliação
  • Chang CW; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Zhou S; Department of Radiation Oncology, Physics Division, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, United States of America.
  • Gao Y; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Lin L; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Liu T; Department of Radiation Oncology, Mount Sinai Medical Center, New York, NY 10029, United States of America.
  • Bradley JD; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Zhang T; Department of Radiation Oncology, Physics Division, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, United States of America.
  • Zhou J; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Yang X; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Phys Med Biol ; 67(21)2022 10 19.
Article em En | MEDLINE | ID: mdl-36174551
ABSTRACT
Objective. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization models.Approach.The proposed framework includes two experiments to validatein vivodose and water equivalent thickness (WET) distributions using anthropomorphic and porcine phantoms. Phantoms were irradiated using anteroposterior proton beams, and the exit doses and residual ranges were measured by MatriXX PT and a multi-layer strip ionization chamber. Two pre-trained conventional and physics-informed residual networks (RN/PRN) were used for mass density inference from DECT. Additional two heuristic material conversion models using single-energy CT (SECT) and DECT were implemented for comparisons. The gamma index was used for dose comparisons with criteria of 3%/3 mm (10% dose threshold).Main results. The phantom study showed that MCDC with PRN achieved mean gamma passing rates of 95.9% and 97.8% for the anthropomorphic and porcine phantoms. The rates were 86.0% and 79.7% for MCDC with the empirical DECT model. WET analyses indicated that the mean WET variations between measurement and simulation were -1.66 mm, -2.48 mm, and -0.06 mm for MCDC using a Hounsfield look-up table with SECT and empirical and PRN models with DECT. Validation experiments indicated that MCDC with PRN achieved consistent dose and WET distributions with measurement.Significance. The proposed framework can be used to identify the optimal CT-based material characterization model for MCDC to improve proton range uncertainty. The framework can systematically verify the accuracy of proton treatment planning, and it can potentially be implemented in the treatment room to be instrumental in online adaptive treatment planning.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Terapia com Prótons / Aprendizado Profundo Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Animals Idioma: En Revista: Phys Med Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Terapia com Prótons / Aprendizado Profundo Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Animals Idioma: En Revista: Phys Med Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos