Simulation of the Secondary Neutrons Production in a Soft Tissue Phantom Produced by Protons and Carbon Ions: Utilizing the PHITS Monte Carlo Code for a Comparative Study

EasyChair Preprint 9638

Abstract

Hadron therapy (HT) is a technique that often uses accelerated ions to destroy tumors, such as protons and carbon ions, and is highly successful for some radiation-resistant cancers. Despite the fact that most of the dose in HT is delivered to the tumor volume through electromagnetic interactions with atomic electrons which allows for excellent dose distributions, consequently, tumors located near critical organs become the indicator of choice compared to photon radiotherapy. However, the nuclear reactions induced by the primary particles can generate undesirable secondary radiation interactions. As a result, a significant portion of the patient’s body may be exposed to the secondary background radiation field which increases the secondary cancer risk, particularly relevant for pediatric or re-irradiated patients. Thus, these unwanted secondary particles should be evaluated. The purpose of this research is to evaluate the dose of protons and carbon ions by measurements of dose distributions of secondary particles especially those produced in the patient’s tissue by using two energies 135 MeV proton, and 264 MeV/u carbon ions beam(¹²C) in a soft tissue phantom. The Bragg peak position and dose distribution were compared. We used for this research the Monte Carlo radiation transport code, called Particle and Heavy Ion Transport code System (PHITS).

Keyphrases: Bragg peak, Hadron Therapy, dose, nuclear reactions, secondary cancer risk, secondary radiation

@booklet{EasyChair:9638,
  author    = {Mohamed El-Asery and Zouhair Sadoune and Hassane El Bekkouri and Adil Bardane and Abdessamad Didi and El Mehdi Alibrahimi and Chakir El Mahjoub},
  title     = {Simulation of the Secondary Neutrons Production in a Soft Tissue Phantom Produced by Protons and Carbon Ions: Utilizing the PHITS Monte Carlo Code for a Comparative Study},
  howpublished = {EasyChair Preprint 9638},
  year      = {EasyChair, 2023}}