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21 April 2020 The Impact of Tissue Type and Density on Dose Point Kernels for Patient-Specific Voxel-Wise Dosimetry: A Monte Carlo Investigation
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Abstract

We report the generation of dose point kernels for clinically-relevant radionuclide beta decays and monoenergetic electrons in various tissues to understand the impact of tissue type on dose point kernels. Currently available voxel-wise dosimetry approaches using dose point kernels ignore tissue composition and density heterogeneities. Therefore, the study on the impact of tissue type on dose point kernels is warranted. Simulations were performed using the GATE Monte Carlo toolkit, which encapsulates GEANT4 libraries. Dose point kernels were simulated in phantoms of water, compact bone, lung, adipose tissue, blood and red marrow for radionuclides 90Y, 188Re, 32P, 89Sr, 186Re, 153Sm and 177Lu and monoenergetic electrons (0.015–10 MeV). All simulations were performed by assuming an isotropic point source of electrons at the center of a homogeneous spherical phantom. Tissue-specific differences between kernels were investigated by normalizing kernels for effective pathlength. Transport of 20 million particles was found to provide sufficient statistical precision in all simulated kernels. The simulated dose point kernels demonstrate excellent agreement with other Monte Carlo packages. Deviation from kernels reported in the literature did not exceed a 10% global difference, which is consistent with the variability among published results. There are no significant differences between the dose point kernel in water and kernels in other tissues that have been scaled to account for density; however, tissue density predictably demonstrated itself to be a significant variable in dose point kernel distribution.

©2020 by Radiation Research Society. All rights of reproduction in any form reserved.
Ashok Tiwari, Stephen A. Gravesa, and John Sunderland "The Impact of Tissue Type and Density on Dose Point Kernels for Patient-Specific Voxel-Wise Dosimetry: A Monte Carlo Investigation," Radiation Research 193(6), 531-542, (21 April 2020). https://doi.org/10.1667/RR15563.1
Received: 11 November 2019; Accepted: 11 March 2020; Published: 21 April 2020
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