Monte Carlo Source Modeling for Deterministic Dose Deposition
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Solution Overview
Problem
Existing models for dose deposition in radiotherapy, especially for small tumors, face accuracy issues due to broad assumptions and empirical methods, which can lead to suboptimal treatment plans and increased damage to healthy tissue.
Innovation Solution
A method that uses Monte Carlo (MC) simulations to model the behavior of radiation particles from advanced beam/linear accelerator geometries, coupled with fast dosing algorithms to improve accuracy and reduce calculation time, allowing for more precise dose deposition calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulations are used to model radiation behavior, then accuracy of dose calculations is improved, but calculation time increases significantly
Solution Approach 1:
The patent segments the calculation process into two distinct phases: (1) a pre-computation phase using Monte Carlo simulations to generate particle behavior data and source models, and (2) a clinical execution phase using deterministic methods that leverage the pre-computed data. This segmentation allows the computationally intensive MC simulations to be performed once offline, while clinical calculations use the pre-computed models for rapid results.
Solution Approach 2:
The patent performs preliminary Monte Carlo simulations to pre-compute particle behavior data, source models, and dose deposition patterns before clinical use. These pre-computed results are stored and reused during treatment planning, eliminating the need to run full MC simulations for each clinical case and significantly reducing calculation time while maintaining accuracy.
2Productivity
If deterministic methods are used to simulate radiation behavior, then calculation speed is improved, but accuracy decreases
Solution Approach 1:
The patent introduces pre-computed Monte Carlo source models as an intermediary between the physics of radiation transport and the deterministic dose calculation algorithms. These source models contain accurate particle behavior data generated by MC simulations, which serve as input for deterministic methods, thereby enabling fast calculations to produce accurate results.
Solution Approach 2:
The patent changes the parameters and data structure of the source model to be compatible with deterministic algorithms. By reformulating the Monte Carlo particle behavior data into a format suitable for deterministic transport equations, the system enables fast deterministic calculations to achieve MC-level accuracy without requiring full MC simulation complexity.
3Use of energy by moving object
If empirical models are used for dose deposition, then computational resources are reduced, but accuracy for small tumors deteriorates
Solution Approach 1:
The patent applies local quality by using particle-specific and geometry-specific source models that are tailored to the particular beam configuration and patient anatomy. Instead of using general empirical models, the system computes and applies customized source models that accurately represent the specific radiation transport characteristics for each treatment scenario, particularly improving accuracy for small tumors where local variations are critical.
Data Source
AI summary
Embodiments described herein provide for coupling Monte Carlo source modeling with deterministic dose calculations. An internal volumetric first scatter distributed source of a patient is determined using Monte Carlo simulations and ingested into one or more dosing algorithms. The dosing algorithms use the source model to determine a dose deposition.