Radiation Leaf-Sequence Plan Optimization via Subset Segmentation
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Solution Overview
Problem
Optimizing radiation-treatment leaf-sequence plans with a large number of fluence-based control points is computationally challenging, leading to time-consuming processes that result in delays, increased costs, and patient discomfort due to equipment downtime.
Innovation Solution
A method involving identifying a set of fluence-based control points, selecting and optimizing subsets iteratively using techniques like steepest descent or simulated annealing, and combining them to achieve a fully optimized set, which can be used to specify a radiation-treatment leaf-sequence plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fluence-based control points are used to optimize the leaf-sequence plan, then the radiation treatment accuracy is improved, but the optimization time increases significantly
Solution Approach 1:
The patent divides the large set of fluence-based control points into multiple smaller subsets. Each subset is optimized separately and independently, allowing parallel processing and reducing the overall computational time while maintaining the accuracy benefits of fluence-based optimization.
Solution Approach 2:
The patent performs preliminary selection of control points that have the greatest impact on treatment accuracy. By identifying and prioritizing critical control points before optimization, the system achieves high accuracy with fewer computational iterations, reducing optimization time.
2Manufacturing precision
If a large number of fluence-based control points are optimized simultaneously, then the treatment plan accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational task by dividing control points into subsets that can be processed independently. This reduces the computational complexity of each optimization step while preserving the overall treatment plan accuracy through subsequent combination of subset results.
Solution Approach 2:
The patent optimizes only the most critical subsets of control points in detail, while other less critical control points are optimized with reduced precision or using simplified models. This partial action approach maintains sufficient treatment accuracy while significantly reducing computational complexity.
3Productivity
If traditional optimization methods are used for leaf-sequence plans, then the computational load is manageable, but the optimization results are less accurate and take longer
Solution Approach 1:
By segmenting the control point set into manageable subsets, the patent enables the use of accurate fluence-based optimization methods on smaller data sets. This maintains computational efficiency while achieving superior accuracy compared to traditional methods applied to the entire set.
Solution Approach 2:
The patent changes the optimization parameters by working with subset-specific fluence distributions rather than global parameters. This allows more precise local optimization while reducing the overall computational burden, achieving both speed and accuracy improvements.
Data Source
AI summary
These teachings provide for identifying (101) a set of fluence-based control points to represent a leaf sequence and then selecting (102) a first subset of the fluence-based control points and optimizing that first subset. This first subset (now optimized) is combined (103) with a second subset of the fluence-based control points and the aggregation then optimized. The latter activities are then iteratively repeated (104) with additional subsets of the fluence-based control points to provide resultant optimized sets of fluence-based control points. This eventually results in a fully optimized complete set of fluence-based control points. This optimized set of fluence-based control points are then used (105) to specify a corresponding radiation-treatment leaf-sequence plan.