Particle Transport Simulation for Radiotherapy Dose Accuracy
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Solution Overview
Problem
Current radiotherapy methods, such as the semi-empirical analytic method and Monte Carlo method, face limitations in accuracy and computational efficiency for simulating particle transport and determining human doses, with the Monte Carlo method being time-consuming and requiring large computational resources.
Innovation Solution
A method and apparatus for simulating particle transport by estimating the number of incident particles required, inputting them in batches, recording transport paths, determining uncertainty thresholds, and dynamically adjusting particle input based on standard-reaching rates in regions of interest, while also performing dynamic denoising operations on dose distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Monte Carlo method is used to simulate particle transport, then the accuracy of dose distribution calculation is improved, but the computational time and resources increase significantly
Solution Approach 1:
The simulation domain is divided into multiple lattice cells, and particles are transported and tracked in each cell independently. This segmentation allows the computational task to be distributed and processed in parallel, reducing overall computational time while maintaining the accuracy benefits of the Monte Carlo method in each sub-region.
Solution Approach 2:
The method pre-calculates and stores the transport paths of particles in advance during the simulation process. By preparing path information beforehand and reusing it for dose calculation, the method avoids redundant computations and significantly reduces the time required for final dose distribution determination.
2Reliability
If particles are transported in full batches to ensure coverage of all lattice cells, then the completeness of dose distribution data is improved, but the computational efficiency deteriorates
Solution Approach 1:
The method does not require transporting the full batch of particles through all lattice cells to achieve complete dose distribution data. By using uncertainty thresholds and standard-reaching rate criteria, the simulation can stop early once sufficient statistical accuracy is achieved in the region of interest, performing only the partial action needed rather than complete particle transport through the entire domain.
Solution Approach 2:
The simulation incorporates feedback mechanisms by continuously monitoring the uncertainty of dose calculations in each lattice cell and the standard-reaching rate. When the uncertainty in the region of interest falls below a predetermined threshold or the standard-reaching rate exceeds a set value, the simulation automatically terminates. This feedback-driven approach ensures data completeness while dramatically improving computational efficiency by avoiding unnecessary particle transports.
3Measurement precision
If the uncertainty threshold is set low to ensure high accuracy, then the precision of dose calculation is improved, but the number of required particle transports increases
Solution Approach 1:
The method applies different uncertainty requirements to different regions. By focusing on achieving low uncertainty specifically in the region of interest (ROI) rather than uniformly across all lattice cells, the simulation can maintain high precision where needed while reducing particle transport requirements in less critical areas. The standard-reaching rate metric specifically evaluates whether sufficient particles have been transported to achieve desired precision in the ROI.
Data Source
AI summary
A method for simulating a particle transport may include recording transport paths of inputted particles and determining an uncertainty of each of lattice cells based on the transport paths of each batch of the inputted particles, a lattice cell being a qualified lattice cell if an uncertainty of the lattice cell does not exceed a first threshold; determining a standard-reaching rate of lattice cells in a region of interest (ROI), the ROI including at least one lattice cell, the standard-reaching rate of lattice cells in the ROI being equal to a ratio of the number of qualified lattice cells to a total number of lattice cells in the ROI; and if the standard-reaching rate of lattice cells in the ROI exceeds a second threshold, stopping inputting particles, and outputting the transport paths of the inputted particles.