Abstract
PurposeTo provide fast and accurate dose calculation in voxelized geometries for proton radiation therapy by implementing an adaptive step size algorithm in the proton macro Monte Carlo (pMMC) method.MethodsThe in-house developed local-to-global MMC method for proton dose calculation is extended with an adaptive step size algorithm for efficient proton transport through a voxelized geometry by sampling transport parameters from a pre-simulated database. Adaptive choice of an adequate slab size in dependence of material interfaces in the proton’s longitudinal and lateral vicinity is investigated. The dose calculation algorithm is validated against the non-adaptive pMMC and full MC simulation for pencil and broad beams with various energies impinging on academic phantoms as well as a head and neck patient CT.ResultsFor material interfaces perpendicular to a proton’s direction, choice of nearest neighbor slab thickness shows best trade-off between dosimetric accuracy and calculation efficiency. Adaptive reduction of chosen slab size is shown to be required for material interfaces closer than 0.5 mm in lateral direction. For the academic phantoms, dose differences of within 1% or 1 mm compared to full Geant4 MC simulation are found, while achieving an efficiency gain of up to a factor of 5.6 compared to the non-adaptive algorithm and 284 compared to Geant4. For the head and neck patient CT, dose differences are within 1% or 1 mm with an efficiency gain factor of up to 3.4 compared to the non-adaptive algorithm and 145 compared to Geant4.ConclusionAn adaptive step size algorithm for proton macro Monte Carlo was implemented and evaluated. The dose calculation provides the accuracy of full MC simulations, while achieving an efficiency gain factor of three compared to the non-adaptive algorithm and two orders of magnitude compared to full MC for a complex patient CT.
Highlights
Over the past decade, dozens of new proton therapy facilities have entered clinical operation and hundreds of patients are treated with protons every day [1]
The proton macro Monte Carlo (MC) (MMC) method on the other hand still performs particle transport on a history-by-history base through a medium, sampling energy loss, spatial deflection and hadronic interactions on a macro step basis. This pMMC method still has shortcomings when it comes to voxelized geometries such as a clinical patient computed tomography (CT) dataset, where the applicable macro step size is generally limited to the voxel size due to voxel-by-voxel changes of Hounsfield unit (HU) value
General concept of pMMC and database generation The basic concept of the pMMC proton transport was introduced by Fix et al [14]: To simulate the transport of a proton through a voxelized geometry, consecutive macro steps are applied by sampling transport parameters from probability distribution functions, which are pre-simulated and stored as histograms in a database
Summary
Dozens of new proton therapy facilities have entered clinical operation and hundreds of patients are treated with protons every day [1]. Packages like TOPAS [7] or GATE [8], which are both based on the Geant MC simulation toolkit [9], provide a framework for accurate dose calculation for both research and clinical applications. The proton MMC (pMMC) method on the other hand still performs particle transport on a history-by-history base through a medium, sampling energy loss, spatial deflection and hadronic interactions on a macro step basis This pMMC method still has shortcomings when it comes to voxelized geometries such as a clinical patient computed tomography (CT) dataset, where the applicable macro step size is generally limited to the voxel size due to voxel-by-voxel changes of Hounsfield unit (HU) value
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
