Automatic Radiation Treatment Plan Optimization Objective Generation
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Solution Overview
Problem
Developing radiation treatment plans that meet clinical metrics is a difficult task requiring considerable human input and oversight, as converting clinical goals into optimization objectives is not intuitive and often requires iterative modifications to achieve desired dose distributions.
Innovation Solution
An apparatus and method for automatically generating radiation treatment plan optimization objectives using a control circuit that converts prescribed treatment instructions into optimization objectives, allowing for iterative optimization of radiation therapy plans without manual human intervention, incorporating prioritization and supplemental objectives to achieve desired dose distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual human input and oversight are used to convert clinical goals into optimization objectives, then the treatment plan can be customized to meet specific clinical metrics, but the process becomes time-consuming and complex requiring considerable human expertise
Solution Approach 1:
The system performs self-service by automatically converting clinical goals into optimization objectives without requiring manual human intervention. The computational system analyzes the clinical goal statement and autonomously generates the appropriate optimization objectives, eliminating the need for human experts to manually translate clinical requirements into technical parameters.
Solution Approach 2:
The patent replaces the mechanical human expertise and manual conversion process with an automated computational system. The system uses algorithms and processing circuits to substitute for human expert judgment in converting clinical goals into optimization objectives, thereby reducing time consumption while maintaining precision.
2Manufacturing precision
If manual iterative modifications are made to optimization objectives, then the desired dose distribution can be achieved, but the complexity of the optimization process increases and requires multiple iterations
Solution Approach 1:
The system performs preliminary action by automatically generating an initial set of optimization objectives that are ready for direct use in the optimization process. This pre-computed objective set eliminates the need for manual iterative modifications, as the objectives are prepared in advance based on the clinical goal statement, reducing both complexity and iteration requirements.
Solution Approach 2:
The patent implements feedback mechanisms where the system automatically adjusts and refines optimization objectives based on the optimization process outcomes. The computational system monitors the dose distribution results and automatically modifies objectives if needed, eliminating manual iterative adjustments and reducing overall process complexity.
3Reliability
If human expertise is required for converting clinical goals into optimization objectives, then accurate treatment plans can be generated, but the productivity of treatment plan generation decreases
Solution Approach 1:
The computational system performs self-service by automatically converting clinical goals into optimization objectives without requiring human expertise. The system autonomously analyzes clinical statements and generates accurate objectives, maintaining treatment plan reliability while dramatically increasing productivity by eliminating manual conversion steps.
Solution Approach 2:
The patent replaces the mechanical human expertise process with automated computational algorithms. The system uses processing circuits and software to substitute for human experts in converting clinical goals into optimization objectives, thereby maintaining accuracy while significantly improving treatment plan generation speed and productivity.
Data Source
AI summary
Generally speaking, pursuant to these various embodiments a memory stores prescribed treatment instructions for a particular patient radiation therapy. Those treatment instructions specify clinical metrics for goals pertaining to at least one treatment volume for the particular patient. Either directly or inferentially those clinical metrics are prioritized. A control circuit automatically converts those treatment instructions into resultant radiation treatment plan optimization objectives where the automatic conversion can compatibly comprise a non-convex optimization objective. The control circuit then automatically iteratively optimizes the radiation treatment plan for the particular patient radiation therapy as a function, at least in part, of the aforementioned resultant optimization objectives to thereby produce an optimized radiation treatment plan for the particular patient that a radiation treatment platform then utilizes to administer therapeutic radiation to the particular patient.