Radiation Treatment Plan Generation via Dose Subinterval Partitioning
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Solution Overview
Problem
Current radiation treatment plans lack the ability to effectively balance tumor coverage and minimize radiation to healthy structures, necessitating improved methods for generating plans that mimic a reference dose distribution.
Innovation Solution
The method involves dividing dose characteristics into subintervals and partitioning voxels based on these subintervals, establishing weights for each partition, and specifying optimization functions to generate radiation treatment plans that balance dose distribution and minimize radiation to healthy tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single optimization function is used for the entire ROI, then the treatment plan generation is simpler, but the ability to balance tumor coverage and healthy tissue protection is insufficient
Solution Approach 1:
The patent divides the ROI into multiple partitions based on dose characteristic subintervals, and assigns a separate optimization function to each partition. This segmentation allows different optimization strategies to be applied to different dose regions, improving the balance between tumor coverage and healthy tissue protection while maintaining manageable complexity through modular optimization functions.
2Reliability
If the radiation dose is increased to ensure sufficient tumor treatment, then tumor coverage is improved, but radiation exposure to healthy tissues increases
Solution Approach 1:
The patent applies different optimization functions with different weights to different partitions of the ROI based on their dose characteristics. This local quality approach ensures that tumor regions receive optimized doses for effective treatment while healthy tissue regions receive minimized radiation exposure, thereby resolving the contradiction between tumor treatment effectiveness and healthy tissue protection.
3Reliability
If manual delineation and plan creation is used, then clinical expertise can be applied, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent implements an automated optimization process that uses established weights and optimization functions to generate treatment plans without requiring manual intervention for each plan creation. The system self-adjusts the optimization parameters based on the partitioned dose characteristics, maintaining high treatment plan quality while significantly reducing the time and labor required compared to manual methods.
Data Source
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AI summary
The present disclosure generally relates to the field of radiation treatment. More specifically, the present disclosure generally relates to methods and radiation treatment systems for generating a radiation treatment plan. According to one example embodiment, a method may comprise dividing (220) at least one dose characteristic related to a dose distribution for a ROI into a plurality of subintervals. The method may further comprise partitioning (230) the ROI into a plurality of different partitions based on the subintervals. All voxels of the ROI with values within the same subinterval are partitioned into the same partition. For each of the plurality of different partitions, the method may comprise establishing (240) a weight and specifying (250) an optimization function for an obtainable dose distribution based on the respective subinterval of dose characteristics. The method may further comprise generating (260) the radiation treatment plan based on said established weights and specified optimization functions.