Radiation Treatment Plan Generation Using Contour-to-Dose Mapping
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Solution Overview
Problem
Current radiation therapy treatment plans require significant clinical experience and involve lengthy trial-and-error adjustments, leading to inefficiencies in the design process.
Innovation Solution
A method utilizing deep reinforcement learning and shape matching to automatically determine the optimal target combination, position, and dose within a designated target volume, reducing reliance on human experience and eliminating repetitive adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicists manually design treatment plans based on clinical experience, then the treatment plan quality can be ensured, but the time consumption and complexity of the process increase significantly
Solution Approach 1:
The system pre-calculates and stores optimal target combinations, positions, and doses for various target volume contours in advance through deep reinforcement learning training. When generating a treatment plan, the system directly retrieves pre-computed results matching the patient's target contour, eliminating the need for real-time manual trial-and-error adjustments while ensuring clinically validated treatment quality
Solution Approach 2:
The system enables automatic treatment plan generation by training an intelligent agent through deep reinforcement learning to autonomously determine optimal target configurations, positions, and doses. The trained model independently processes new patient cases without requiring physicist intervention, achieving both high efficiency and reliable treatment plan quality through self-learning from historical clinical data
2Manufacturing precision
If physicists perform trial-and-error adjustments to ensure prescribed doses, then the dose accuracy can be improved, but the number of adjustments and time required increase
Solution Approach 1:
The deep reinforcement learning training process incorporates feedback mechanisms where the agent receives reward signals based on how well its generated target configurations meet the prescribed dose requirements. The agent continuously learns from this feedback, adjusting its strategy to optimize dose accuracy. During actual treatment plan generation, the pre-trained model applies learned dose optimization rules automatically, achieving high dose accuracy without iterative manual adjustments
Solution Approach 2:
The system transforms the complex multi-parameter optimization problem of target number, sizes, positions, and doses into a learned parameter mapping through deep reinforcement learning. The trained model directly predicts optimal parameter configurations based on target contour characteristics, achieving accurate dose delivery while eliminating the need for repeated parameter adjustments that characterize traditional manual planning
3Adaptability or versatility
If manual design methods are used, then flexibility in handling complex cases is maintained, but the automation level and consistency of treatment plans decrease
Solution Approach 1:
The deep reinforcement learning model is trained on diverse target contour data representing various cancer types and anatomical locations, enabling it to handle multiple different clinical scenarios with a single unified system. The model learns generalizable patterns that allow it to adapt to different target shapes, sizes, and locations automatically, providing both high automation and broad adaptability across complex treatment cases
Solution Approach 2:
The system achieves consistent automated treatment plan generation through self-learning from historical clinical data. The intelligent agent independently analyzes target contours, determines optimal target configurations, and generates treatment plans without human intervention. This self-service capability ensures consistent application of optimal planning strategies across all cases while maintaining the ability to handle complex scenarios through learned patterns
Data Source
AI summary
The embodiments of the present application relate to the technical field of medical information. Provided are a treatment plan generation method and apparatus, and a storage medium. The method comprises: acquiring an objective contour of an objective target region; searching a preset target mapping relationship for an objective target set which corresponds to the objective contour, wherein the objective target set comprises the number of targets and the size of each target; determining the position of each target in the objective target region based on the size of each target; determining the position of each target in the objective target region according to the size of each target; and determining the dose of each target according to the position of each target and a preset prescribed dose, and generating a treatment plan. The present application can improve the formulation efficiency of a treatment plan.