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Degradation of proton depth dose distributions attributable to microstructures in lung‐equivalent material
Author(s) -
Titt Uwe,
Sell Martin,
Unkelbach Jan,
Bangert Mark,
Mirkovic Dragan,
Oelfke Uwe,
Mohan Radhe
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4932625
Subject(s) - monte carlo method , imaging phantom , materials science , proton , proton therapy , bragg peak , image resolution , computational physics , nuclear medicine , molecular physics , optics , physics , mathematics , statistics , nuclear physics , medicine
Purpose: The purpose of the work reported here was to investigate the influence of sub‐millimeter size heterogeneities on the degradation of the distal edges of proton beams and to validate Monte Carlo (MC) methods’ ability to correctly predict such degradation. Methods: A custom‐designed high‐resolution plastic phantom approximating highly heterogeneous, lung‐like structures was employed in measurements and in Monte Carlo simulations to evaluate the degradation of proton Bragg curves penetrating heterogeneous media. Results: Significant differences in distal falloff widths and in peak dose values were observed in the measured and the Monte Carlo simulated curves compared to pristine proton Bragg curves. Furthermore, differences between simulations of beams penetrating CT images of the phantom did not agree well with the corresponding experimental differences. The distal falloff widths in CT image‐based geometries were underestimated by up to 0.2 cm in water (corresponding to 0.8–1.4 cm in lung tissue), and the peak dose values of pristine proton beams were overestimated by as much as ˜35% compared to measured curves or depth‐dose curves simulated on the basis of true geometry. The authors demonstrate that these discrepancies were caused by the limited spatial resolution of CT images that served as a basis for dose calculations and lead to underestimation of the impact of the fine structure of tissue heterogeneities. A convolution model was successfully applied to mitigate the underestimation. Conclusions: The results of this study justify further development of models to better represent heterogeneity effects in soft‐tissue geometries, such as lung, and to correct systematic underestimation of the degradation of the distal edge of proton doses.