Biological Dose Estimation for Charged-Particle Therapy using an Improved PHITS Code Coupled with a Microdosimetric Kinetic Model
Journal article, 2009

Microdosimetric quantities such as lineal energy, y, are better indexes for expressing the RBE of HZE particles in comparison to LET. However, the use of microdosimetric quantities in computational dosimetry is severely limited because of the difficulty in calculating their probability densities in macroscopic matter. We therefore improved the particle transport simulation code PHITS, providing it with the capability of estimating the microdosimetric probability densities in a macroscopic framework by incorporating a mathematical function that can instantaneously calculate the probability densities around the trajectory of HZE particles with a precision equivalent to that of a microscopic track-structure simulation. A new method for estimating biological dose, the product of physical dose and RBE, from charged-particle therapy was established using the improved PHITS coupled with a microdosimetric kinetic model. The accuracy of the biological dose estimated by this method was tested by comparing the calculated physical doses and RBE values with the corresponding data measured in a slab phantom irradiated with several kinds of HZE particles. The simulation technique established in this study will help to optimize the treatment planning of charged-particle therapy, thereby maximizing the therapeutic effect on tumors while minimizing unintended harmful effects on surrounding normal tissues.

Author

T. Sato

Japan Atomic Energy Agency

Research Group for Radiation Protection

Y Kase

National Institute of Radiological Sciences

R Watanabe

Japan Atomic Energy Agency

K. Niita

Research Organization for Information Science and Technology

Lembit Sihver

Chalmers, Applied Physics, Nuclear Engineering

Radiation Research

0033-7587 (ISSN) 19385404 (eISSN)

Vol. 171 1 107-117

Subject Categories

Subatomic Physics

DOI

10.1667/RR1510.1

More information

Created

10/7/2017