Radiation Treatment Plan Optimization Using 3D Dose Constraints
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Solution Overview
Problem
Existing radiation treatment plans lack an efficient method to discriminate between target volumes and adjacent tissues, leading to laborious, time-consuming, and error-prone optimization processes.
Innovation Solution
A control circuit optimizes radiation treatment plans using predicted three-dimensional radiation dose distribution information as a constraint, incorporating clinical goals and other patient-specific data to guide the optimization algorithm, reducing the need for multiple planning attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional radiation treatment planning is performed manually through multiple planning attempts, then the treatment plan can be optimized to achieve desired dose distribution, but the process becomes laborious, time-consuming, and error-prone
Solution Approach 1:
The optimization algorithm automatically adjusts treatment parameters and calculates dose distributions without requiring manual intervention for each planning attempt. The system self-optimizes by iteratively adjusting parameters based on predicted dose distributions and clinical goals, eliminating the need for repeated manual planning attempts while maintaining high dose distribution accuracy
Solution Approach 2:
The patent replaces manual mechanical planning processes with an automated computational optimization algorithm. The algorithm uses mathematical models to predict dose distributions and automatically adjusts treatment parameters, substituting the manual iterative planning process with an automated system that achieves similar or better optimization results faster
2Productivity
If automated optimization algorithms are used to generate treatment plans, then planning time is reduced, but the algorithms may not inherently discriminate between target volumes and adjacent tissues
Solution Approach 1:
The optimization algorithm incorporates feedback from predicted dose distributions and clinical goals to iteratively adjust treatment parameters. The system uses feedback from dose prediction models to refine the optimization process, ensuring that target volumes receive appropriate doses while adjacent tissues are protected, thereby achieving both efficiency and precision
Solution Approach 2:
The patent applies different optimization criteria and constraints to different regions of the treatment plan. The algorithm considers local characteristics of target volumes versus adjacent tissues when adjusting parameters, applying stricter dose constraints to critical structures and optimized dosing to target areas, thereby achieving local quality differentiation
3Reliability
If treatment parameters are adjusted to increase dose to target volume, then therapeutic effect improves, but collateral effects on surrounding tissues increase
Solution Approach 1:
The optimization algorithm systematically adjusts multiple treatment parameters simultaneously (beam angles, doses, field sizes) to find the optimal balance between delivering sufficient dose to the target volume and limiting exposure to surrounding tissues. By changing parameters in a coordinated manner based on predicted dose distributions, the system achieves reliable therapeutic effects while minimizing collateral damage
Data Source
AI summary
A control circuit can access predicted three-dimensional radiation dose distribution information and optimize a radiation treatment plan as a function, at least in part, of the predicted three-dimensional radiation dose distribution information to thereby prompt optimization towards the predicted three-dimensional radiation dose distribution information. By one approach, these teachings will support using the predicted three-dimensional radiation dose distribution information as an optimization constraint.