Citation

Moradi, Farhad and Taheri, Ali and Abdul Rashid, Hairul Azhar and Bradley, David Andrew (2026) Monte Carlo investigation of source motion in remote afterloading brachytherapy. Radiation Physics and Chemistry, 239. p. 113387. ISSN 0969806X

Text Monte Carlo investigation of source motion in remote afterloading brachytherapy - ScienceDirect.pdf - Published Version Restricted to Repository staff only Download (280kB)

Abstract

High-dose-rate (HDR) remote afterloading brachytherapy delivers precise radiation doses to tumor sites, but traditional treatment planning systems (TPS) often overlook source motion, leading to discrepancies between planned and delivered doses. This study introduces a novel application of the TOPAS Monte Carlo code's Time Features to model 192Ir source dynamics in a tissue-equivalent phantom, using discrete time steps to simulate motion. We aimed to identify optimal temporal resolutions that ensure dosimetric accuracy of MC simulations while minimizing computational demands. A cylindrical soft-tissue phantom with a spherical tumor and adjacent organ at risk (OAR) was simulated, with the source moving through a central applicator at speeds of 30, 45, and 60 cm/s. Time step sizes from 1 to 150 ms were tested, revealing saturation points at 25 ms for 30 and 45 cm/s, and 10 ms for 60 cm/s, beyond which tumor-to-OAR dose ratios changed by <1 %. Transit dose contributed 24–27 % to total tumor dose, expectedly decreasing with higher speeds. Faster source transit also enhanced the therapeutic index, with tumor-to-OAR dose ratios of 2.7–3.0, 3.1–3.5, and 3.3–3.8 for 30, 45, and 60 cm/s, respectively, and reduced excess irradiation compared to static source models. These findings highlight the importance of transit dose in treatment planning and provide optimized time resolution parameters for dynamic brachytherapy simulations, potentially enhancing clinical dose accuracy.

Item Type:	Article
Uncontrolled Keywords:	Brachytherapy
Subjects:	R Medicine > RC Internal medicine
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Dec 2025 08:33
Last Modified:	10 Dec 2025 08:33
URII:	http://shdl.mmu.edu.my/id/eprint/15054

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