Monte Carlo Influence Matrix Generation for Radiation Therapy
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Solution Overview
Problem
Generating influence matrices for radiation treatment plans is computationally intensive and time-consuming due to the need to consider billions of contributions from thousands of spots to millions of voxels, limiting the practicality of their use in optimization procedures.
Innovation Solution
Integrating Monte Carlo particle transport simulation to quickly generate influence matrices by calculating the dose deposited by each particle and adding it to the corresponding influence matrix elements, reducing computational requirements through precomputation of spot and voxel weights and masses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate influence matrices by calculating billions of contributions from thousands of spots to millions of voxels, then measurement precision is maintained, but productivity deteriorates due to extremely long computation time
Solution Approach 1:
The patent pre-calculates and stores the path information of particles (protons) through the transport volume before generating the influence matrix. By preparing particle tracks in advance and organizing them into a condensed format, the system avoids recalculating billions of particle contributions during influence matrix generation, thus maintaining precision while dramatically reducing computation time
Solution Approach 2:
The patent creates a condensed copy of particle transport information by recording the path of each particle through voxels in a simplified data structure. Instead of performing full Monte Carlo simulations during influence matrix generation, the system uses pre-recorded particle tracks that capture the essential dose deposition information, enabling rapid calculation without sacrificing accuracy
2Measurement precision
If traditional Monte Carlo simulation methods are used to calculate dose contributions, then measurement precision is maintained, but loss of time worsens due to computational intensity
Solution Approach 1:
The system performs preliminary Monte Carlo simulations to generate and store particle transport paths before influence matrix generation. By pre-computing particle tracks and organizing them in a condensed format that records voxel intersections and dose contributions, the system eliminates the need for repeated Monte Carlo calculations during influence matrix assembly, reducing computation time while preserving dosimetric accuracy
Solution Approach 2:
The patent implements a dynamic approach where particle transport information is captured during a preliminary simulation phase and then statically stored for rapid reuse. The condensed particle track data structure allows the system to dynamically access pre-computed information during influence matrix generation, avoiding the static computational burden of repeated full Monte Carlo simulations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Significantly reduces the time and resource requirements for radiation treatment plan optimization, making influence matrices feasible for use in contexts previously impractical, and speeds up the optimization process by several orders of magnitude.
Implementation Method 1
Integrating Monte Carlo particle transport simulation to quickly generate influence matrices by calculating the dose deposited by each particle and adding it to the corresponding influence matrix elements
Data Source
AI summary
These teachings provide for quickly yet accurately forming an influence matrix by generating the influence matrix via integration with a Monte Carlo particle transport simulation. The resultant influence matrix can then be utilized in an ordinary manner when optimizing a radiation treatment plan. By one approach, the foregoing comprises generating the influence matrix via integration with a Monte Carlo particle transport simulation on a particle-by-particle basis. For example, for each particle, these teachings can provide for identifying a spot to which the particle belongs and then adding a dose deposited by the particle during transport to an influence matrix element that corresponds to a spot to which the particle belongs and a voxel to where the dose was deposited.