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Monte Carlo simulation of Cherenkov imaging for Total Skin Electron Treatment with CT DICOM realistic patient geometry.
Zhong, Weili; Ong, Yihong; Miao, Tianshu; Pogue, Brian W; Zhu, Timothy C.
Afiliación
  • Zhong W; Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA, PA 19104.
  • Ong Y; Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA, PA 19104.
  • Miao T; Yale School of Medicine, Yale University, New Haven, CT USA, 06520.
  • Pogue BW; Thayer School of Engineering, Dartmouth College, Hanover, NH USA, 03755.
  • Zhu TC; Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA, PA 19104.
Article en En | MEDLINE | ID: mdl-35506008
ABSTRACT
This Monte Carlo (MC) simulation study provides an evaluation of dose uniformity in a patient and the difference between dose and Cherenkov distributions, which is invaluable in developing conversion factors to relate observed Cherenkov images to actual dose distributions for TSET patients. This MC simulations with TOPAS is performed using realistic patient geometries obtained with a 3D scanner during total skin electron treatments (TSET) at UPenn. For each treatment posture in the Stanford technique, the differences between Cherenkov photon distributions and dose distributions produced in MC are consistent with the differences observed between a Cherenkov imaging camera and in-vivo dose measurement with OSLD on patient skin. According to MC studies of a flat rectangular PVC board, the difference between Cherenkov and dose is mostly due to the spoiler. This is confirmed by observing consistent dose and Cherenkov distributions in clinical measurements on a PVC board without the spoiler. The accumulated dose and Cherenkov distributions for each patient are obtained by projecting the MC output of the 6 postures of the TSET treatment together onto a finite element model of the patient.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc SPIE Int Soc Opt Eng Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc SPIE Int Soc Opt Eng Año: 2022 Tipo del documento: Article