Generative Model for Radiotherapy Phase Space Simulation
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Solution Overview
Problem
Current radiation therapy treatment planning is time-consuming and complex due to the need for manual adjustment of planning constraints, which can result in variable dose distribution and quality, especially with multiple organs at risk (OARs), and relies on computationally intensive Monte Carlo simulations that slow down the simulation of dose deposition in a region of interest.
Innovation Solution
A generative machine learning model, such as a neural network (e.g., GAN, VAE, or normalizing flow network), is trained on phase space data from Monte Carlo simulations to predict and generate samples of particle propagation and scattering, allowing for direct simulation of dose deposition without relying on pre-calculated data from non-volatile storage, thereby reducing treatment plan creation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulations are used to calculate phase space data, then dosimetric accuracy is improved, but computational time and complexity increase
Solution Approach 1:
The patent pre-calculates phase space data using Monte Carlo simulations and stores it in lookup tables before treatment planning. This preliminary computation allows rapid retrieval during actual treatment planning without repeating the computationally intensive simulations, thus maintaining dosimetric accuracy while reducing computational time.
Solution Approach 2:
The patent creates simplified copies of the complex phase space data by generating lookup tables that contain pre-computed dosimetric information. These lookup tables serve as condensed representations that can be quickly accessed and applied during treatment planning, avoiding the need to perform full Monte Carlo simulations in real-time.
2Measurement precision
If pre-calculated phase space data is stored in non-volatile storage, then dosimetric accuracy is maintained, but access time and computational complexity increase
Solution Approach 1:
The patent segments the phase space data into discrete lookup tables organized by beam energy, field size, and other parameters. This segmentation allows the system to access only the specific pre-calculated data needed for each treatment scenario rather than searching through entire datasets, significantly improving access speed while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary organization and indexing of phase space data into structured lookup tables during system initialization. This pre-arrangement enables rapid retrieval during treatment planning by directly accessing pre-positioned data structures, eliminating the need for complex real-time data processing.
3Reliability
If manual adjustment of planning constraints is performed, then clinical acceptability is improved, but treatment planning time increases
Solution Approach 1:
The patent implements an automated iterative optimization system that uses feedback from dosimetric calculations to automatically adjust planning constraints. The system evaluates dose distribution against clinical objectives and automatically modifies beam parameters, reducing the need for manual trial-and-error adjustments while maintaining clinical acceptability.
Solution Approach 2:
The patent enables the treatment planning system to automatically perform constraint optimization and dose calculation using pre-computed lookup tables. The system serves itself by autonomously adjusting planning parameters based on pre-stored dosimetric data, reducing dependence on manual planner intervention while maintaining treatment quality.
Data Source
AI summary
Systems and methods are disclosed for simulating dose deposition. The systems and methods perform operations comprising: receiving a set of training data representing phase space of a radiotherapy treatment device comprising propagation and scattering of particles inside the radiotherapy treatment device; training a generative machine learning model based on the set of training data to generate one or more samples of the phase space of the radiotherapy treatment device; and simulating dose deposition at a particular region of interest based on the one or more samples of the phase space generated by the generative machine learning model.