Impact of porous lung substitute on linear energy transfer (LET) assessed via Monte Carlo simulation and CR-39 measurement with a carbon-ion beam.

Abstract

Background: Carbon-ion beam radiotherapy offers substantial physical and biological advantages due to its distinct Bragg peak (BP) depth dose distribution and higher linear energy transfer (LET) in the peak region that enhances its efficacy in tumor eradication compared to x-ray beams. Porous structures, such as those found in lung and lung-equivalent tissues, unfortunately, introduce significant uncertainties in both dose and LET distributions, which current treatment planning systems (TPS) inadequately address.

Purpose: This study aims to investigate the effects of porous lung-equivalent structures on LET distribution using Monte Carlo (MC) simulations and CR-39 measurements. It seeks to understand how porous structures influence LET spectra and dose-averaged LET (LET_d) in carbon-ion beams.

Methods: A Gammex LN300 phantom and a binary voxel virtual phantom composed of water and air were used to represent lung-equivalent tissues for measurements and MC simulations. LET spectra measured with CR-39 at different depths within the LN300 slabs were compared with MC-calculated LET_d distributions. The impact of porous structures on dose and LET_d distributions was evaluated using various beam configurations, including single-beam and multi-beam setups. Additionally, a convolution method with modulation power (P_mod) was proposed to improve LET_d prediction in porous media.

Results: The study demonstrated that porous structures broaden both the dose and LET_d distributions, especially around the BP region. Multiple beam angles helped mitigate dose degradation but did not resolve discrepancies in the LET_d distributions. Compared with calculation results based on CT images, intensity-modulated particle therapy (IMPT) using a distal LET_d patching method in porous structure increased the median LET_d in the target from 67.2 to 69.6 keV/µm, and the minimum LET_d from 51.5 to 58.0 keV/µm, respectively. Moreover, to improve the prediction of LET_d in porous structures, analytical convolution-based predictions showed good agreement with the MC simulations, with mean LET_d deviations of -1.9% ± 1.6% in the plateau, -3.1% ± 4.9% in the BP, and -1.1% ± 7.7% in the tail region.

Conclusions: Porous lung-equivalent structures significantly affect LET_d distributions in carbon-ion therapy, as confirmed by both CR-39 measurements and MC simulations. IMPT with LET_d optimization may be more impacted by porous structures in terms of median and minimum LET_d values within the target. The Gaussian convolution function shows promise for enhancing LET_d calculation accuracy, but further validation in anatomically complex models is needed to assess its clinical feasibility.