Radiation Treatment Planning Cost Function Optimization
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Solution Overview
Problem
Current radiation treatment planning methods are time-consuming and computationally burdensome, requiring extensive user expertise in handling cost functions, which hampers the optimization process and leads to lengthy planning times.
Innovation Solution
A computer-implemented method for radiation treatment planning that involves receiving a reference objective, selecting a cost function with an assigned initial weight value based on its sensitivity, and applying an optimization procedure to minimize the cost function, thereby automating the handling of cost functions and improving optimization speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional optimization procedures are used for radiation treatment planning, then treatment plan quality can be improved, but planning time and computational burden increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically selecting appropriate cost functions and assigning initial weight values before the optimization procedure begins. This pre-configuration based on sensitivity analysis of dose distribution reduces the time required during the actual optimization process while maintaining treatment plan quality.
Solution Approach 2:
The optimization system performs self-service by automatically selecting cost functions and determining initial weight values without requiring extensive manual configuration by users. The system evaluates dose distribution sensitivity and configures optimization parameters autonomously, reducing both planning time and computational burden while maintaining plan quality.
2Measurement precision
If multiple cost functions with different sensitivities are used in optimization, then optimization accuracy improves, but device complexity and user expertise requirements increase
Solution Approach 1:
The system introduces an intermediary mechanism that automatically selects and weights multiple cost functions based on their sensitivity to dose distribution. This intermediary layer manages the complexity of multiple cost functions by providing a systematic approach to their selection and combination, improving optimization accuracy without requiring users to directly manage the complexity.
Solution Approach 2:
The system dynamically changes parameters by adjusting the weight values assigned to different cost functions based on their sensitivity analysis. This parameter adaptation allows the optimization procedure to effectively utilize multiple cost functions with different sensitivities, improving accuracy while the system automatically manages the complexity of parameter configuration.
3Ease of operation
If manual trial-and-error optimization is performed, then user control over treatment objectives is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system performs self-service by automatically selecting cost functions and configuring optimization parameters based on sensitivity analysis, eliminating the need for manual trial-and-error procedures. This autonomous configuration significantly improves planning efficiency while the system maintains flexibility to accommodate user-defined treatment objectives.
Solution Approach 2:
The system implements feedback mechanisms by evaluating dose distribution sensitivity and using this information to automatically adjust cost function weights and selection. This feedback-driven approach replaces manual trial-and-error with an automated iterative process that improves productivity while maintaining user control over treatment goals through the optimization framework.
Data Source
AI summary
A computer-implemented method may be provided to aid in radiation treatment planning, the method comprising: receiving a reference objective, the reference objective representing a goal to be achieved by a radiotherapy system; selecting a cost function associated with the reference objective from a plurality of cost functions, the selected cost function being associated with an assigned initial weight value, the assigned initial weight value corresponding to sensitivity of an example dose distribution relative to changes in the selected cost function; applying an optimization procedure to a radiation treatment plan for radiation treatment in a patient according to the received reference objective, wherein the optimization procedure uses the assigned initial weight value of the selected cost function and seeks to minimize the selected cost function.