Monte Carlo Proton Therapy Dose Calculation via Distribution Functions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current radiation therapy dose calculators for proton therapy are slow and sacrifice accuracy for faster computation, failing to provide both speed and precision simultaneously.
Innovation Solution
A method and system that employ selected probability density functions and cumulative distribution functions to reduce the complexity of full physics models, using hardware acceleration techniques and look-up tables to streamline Monte Carlo simulations, allowing for faster and more accurate dose computations by selecting the most relevant physics models and simplifying computations based on runtime and accuracy criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full physics models are used for Monte Carlo simulations, then accuracy is improved, but computation time increases significantly
Solution Approach 1:
The patent segments the complete physics model into multiple independent physical aspects (e.g., energy loss, scattering, nuclear interactions). Each aspect is simulated separately using tailored distribution functions, allowing selective replacement of computationally intensive components with faster approximations while maintaining overall accuracy.
Solution Approach 2:
The patent changes the computational parameters by replacing complex physics functions with simplified distribution functions (e.g., Gaussian, Landau, Vavilov distributions) that capture essential physical behavior with reduced computational cost. This parameter transformation enables faster calculations while preserving dose calculation accuracy within acceptable clinical margins.
2Productivity
If distribution functions replace full physics models, then computation speed is improved, but modeling accuracy may deteriorate
Solution Approach 1:
The patent implements a dynamic selection mechanism that adaptively chooses between distribution functions and full physics models based on runtime and accuracy criteria. The system evaluates whether relevant behavior is accurately modeled by the distribution function and selectively applies either the simplified or complete model for each physical aspect, optimizing the balance between speed and accuracy in real-time.
Solution Approach 2:
The patent incorporates feedback mechanisms where simulation results from distribution functions are compared against full physics model results. This feedback loop validates whether the simplified models adequately represent relevant physical behavior, allowing the system to adjust and select the most appropriate modeling approach for each specific case.
Data Source
AI summary
Simulating particle beam interactions includes identifying a set of n functions F1, F2, . . . , Fn corresponding to a plurality of different physical aspects of a particle beam, performing simulations of each Fi using a full physics model, selecting for each Fi a distribution function fi that models relevant behavior and reducing computation of the full physics model for each Fi by replacing Fi with a distribution function fi. The computation reduction includes comparing a set of simulations wherein each fi replaces its respective Fi to determine if relevant behavior is accurately modeled and selecting one of fi or Fi for each n, for a Monte Carlo simulation based on a runtime and accuracy criteria.