GPU-Optimized Multi-Leaf Collimator Leaf Sequence Planning
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Solution Overview
Problem
Current radiation treatment plans for cancer therapy often require significant time to optimize due to the complex optimization of multi-leaf collimator movements, which can consume a disproportionate amount of time in the overall optimization process, especially when trying to restrict energy application to a target volume while minimizing collateral effects on adjacent tissues.
Innovation Solution
The use of a graphics processing unit (GPU) configured as a control circuit to facilitate highly parallel optimization of leaf movements in multi-leaf collimators, either through metaheuristic approaches like differential evolution or parallel explicit integration, allowing for faster and more efficient optimization of radiation treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional serial optimization algorithms are used to optimize multi-leaf collimator leaf sequences, then the optimization can be completed with sequential processing, but the overall optimization time becomes excessively long
Solution Approach 1:
The patent segments the optimization problem into two distinct parts: (1) leaf sequence optimization which is computationally intensive and benefits from parallel processing, and (2) dose calculation which follows sequentially. This segmentation allows the use of GPU parallel processing for the most time-consuming portion while maintaining the necessary sequential dependencies for dose accumulation, thereby resolving the contradiction between optimization speed and processing completeness.
Solution Approach 2:
The patent replaces the traditional CPU-based serial optimization mechanism with a GPU-based parallel processing system. The GPU's architecture with thousands of cores enables simultaneous evaluation of multiple leaf sequence candidates, transforming the optimization from a sequential mechanical process into a highly parallel computational process, thus dramatically reducing optimization time.
2Manufacturing precision
If the optimization process includes recurring sub steps to find leaf sequences matching target fluence, then the treatment plan accuracy is improved, but the computational complexity and time consumption increase significantly
Solution Approach 1:
The patent separates the complex optimization into distinct functional segments: leaf sequence generation, objective function evaluation, and dose calculation. Each segment can be independently optimized and processed, with the leaf sequence optimization handled in parallel on GPU and dose calculation performed sequentially, reducing overall process complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary computational framework that bridges the leaf sequence optimization and dose calculation processes. This framework includes intermediate data structures and communication protocols that allow the two processes to interact efficiently without creating bottlenecks, managing the complexity of recurring optimization sub-steps.
Data Source
AI summary
A control circuit that is configured as a graphics processing unit optimizes leaf movements for at least one multi-leaf collimator, and where optimizing the leaf movements is configured as a highly parallel optimization opportunity. By one approach, a second control circuit serves, at least in part, to so configure the optimizing of the leaf movements as the highly parallel optimization opportunity. That second control circuit can itself be configured as a central processing unit (as distinct from, for example, the aforementioned graphics processing unit).