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Biological dose estimation for charged-particle therapy using an improved PHITS code coupled with a microdosimetric
Tatsuhiko Sato1, Yuki Kase, Ritsuko Watanabe
1Research Group for Radiation Protection, Division of Environment and Radiation Sciences, Nuclear Science and Engineering Directorate, Japan Atomic Energy Agency, Tokai, Naka, Ibaraki, Japan. sato.tatsuhiko@jaea.go.jp
This study enhances particle transport simulations for improved biological dose calculations in HZE particle therapy. The new method optimizes treatment planning, maximizing tumor effectiveness and minimizing normal tissue damage.
Area of Science:
- Medical Physics
- Radiation Oncology
- Computational Biology
Background:
- Microdosimetric quantities like lineal energy (y) are superior to LET for RBE assessment of HZE particles.
- Calculating microdosimetric probability densities in macroscopic matter poses significant challenges for computational dosimetry.
Purpose of the Study:
- To improve the PHITS (Particle and Heavy Ion Transport Simulation) code for estimating microdosimetric probability densities in a macroscopic framework.
- To develop a novel method for biological dose estimation in charged-particle therapy using enhanced simulations and a microdosimetric kinetic model.
Main Methods:
- Incorporated a mathematical function into PHITS for instantaneous calculation of probability densities around HZE particle trajectories.
- Coupled the improved PHITS code with a microdosimetric kinetic model to estimate biological dose.
- Validated the method by comparing simulated physical doses and RBE values with experimental data from HZE particle irradiation of a slab phantom.
Main Results:
- The enhanced PHITS code accurately estimates microdosimetric probability densities in a macroscopic framework.
- The new method provides a reliable estimation of biological dose for charged-particle therapy.
- Simulation results showed good agreement with experimental measurements for various HZE particles.
Conclusions:
- The developed simulation technique is effective for optimizing treatment planning in charged-particle therapy.
- This approach can enhance therapeutic efficacy against tumors while reducing damage to surrounding healthy tissues.
- The improved computational tool facilitates more precise and personalized radiation treatments.
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