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Electron Dose Kernels to Account for Secondary Particle Transport in Deterministic Simulations

Ahmad K. Al-Basheer, Glenn E. Sjoden, Monica Ghita

Nuclear Technology / Volume 168 / Number 3 / December 2009 / Pages 906-918

Dose/Dose Rate / Special Issue on the 11th International Conference on Radiation Shielding and the 15th Topical Meeting of the Radiation Protection and Shielding Division (PART 3) / Radiation Protection /

For low-energy photons, charged-particle equilibrium usually exists within the patient treatment volume, in which case the photon absorbed dose D is equal to the collisional kerma Kc; however, this is not true for the dose buildup region near the surface of the patient or at interfaces of dissimilar materials, such as tissue/lung, where corrections for secondary electron transport may be significant. This is readily treated in Monte Carlo codes, yet difficult to treat explicitly in deterministic codes due to the large optical thicknesses and added numerical complexities in reaching convergence in photon-electron transport problems. To properly treat three-dimensional electron transport physics deterministically, yet still achieve reasonably fast and accurate whole-body computation times using high-energy photons, angular-energy-dependent transport "electron dose kernels" (EDK-SN) have been developed. These kernels were derived via full physics Monte Carlo electron transport simulations and are applied using scaling based on rapid deterministic photon solutions over the problem phase-space, thereby accounting for the dose from charged-particle electron transport. As a result, accurate whole-body doses may be rapidly achieved for high-energy photon sources by performing a single deterministic SN multigroup photon calculation on a parallel cluster with PENTRAN, then linking the SN-derived photon fluxes and net currents to Monte Carlo-based EDKs to account for a full physics dose. Water phantom results using a uniform 0- to 8-MeV step uniform beam indicate that the dose can be accurately obtained within the uncertainty of a full physics Monte Carlo simulation. Followup work will implement this method on phantoms.

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