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Acceleration of Proton Monte Carlo Simulations Using the Macro Monte Carlo Method


D Jacqmin

D Jacqmin1*, University of Wisconsin-Madison, Madison, WI

WE-C-BRB-10 Wednesday 10:30:00 AM - 12:30:00 PM Room: Ballroom B

Purpose: This work describes a new macro Monte Carlo method for proton beam dose calculation. Originally developed for electron beams, macro Monte Carlo (MMC) is designed to provide better computational efficiency than traditional MC without sacrificing accuracy.

Methods: The proton MMC method performs dose calculation using pre-calculated steps derived from MCNPX simulations. Simulations of mono-energetic proton pencil beams ranging from 5-200 MeV impinging on homogenous cylinders of varying length, radius and material were carried out using MCNPX. The energy loss, trajectory and exit position of the exiting protons and secondary particles have been determined and used to create a pre-calculated step database. The database is used to transport protons through an absorbing geometry in a step-by-step manner. Energy loss is scored in a 3D dose grid during each step. The creation of secondary particles is tracked as well. For validation purposes, the dose distributions for many monoenergetic proton beams have been calculated using proton MMC and MCNPX for single- and multi-layered phantoms. The resulting central-axis depth dose curves and dose profiles have been compared. The materials tested include water, bone, fat, soft tissue and lung.

Results: The calculated central-axis depth dose curves agree better than 1% or 1 mm for all pencil beams near the Bragg peak. The lateral dose profiles agree to better than 1% or 1 mm for all pencil beams near the Bragg peak. Away from the Bragg peak, the depth dose curves and lateral profiles agree better than 1% or 1 mm for energies below 100 MeV. Above 100 MeV, disagreement can reach 5%. The computational efficiency of proton MMC is about 100 times greater than for MCNPX.

Conclusions: The results from the dose comparisons show that proton MMC is accurate and more efficient than traditional Monte Carlo.

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