Automated Radiation Beam Parameter Optimization for Complex Tumor Conformality
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Solution Overview
Problem
Current radiation treatment planning systems face challenges in delivering precise doses to tumors while minimizing exposure to healthy tissues, especially for larger, irregularly shaped tumors or those near critical structures, due to limitations in beam control and positioning in conventional forward planning methods.
Innovation Solution
The development of an automated system that selects and optimizes radiation beam parameters, including size, shape, and orientation, using inverse planning techniques and spatial nodes, to generate treatment plans that achieve desired dose distributions with improved conformality and homogeneity, leveraging robotic-based radiation delivery systems like the CyberKnife system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If forward planning methods are used with manual beam parameter selection, then the treatment planning process is simpler to operate, but the manufacturing precision of dose distribution to match tumor geometry is insufficient
Solution Approach 1:
The patent applies inverse planning methodology, inverting the traditional forward planning approach. Instead of manually selecting beam parameters and calculating resulting dose distributions, the system starts with desired dose distribution constraints and automatically computes the optimal beam parameters (angles, weights, shapes) to achieve those constraints. This inversion enables automated optimization of dose conformality to complex tumor geometries while reducing manual iteration.
Solution Approach 2:
The system automatically varies multiple beam parameters simultaneously (beam angles, intensities, shapes, and positions) to optimize dose distribution. By computationally exploring the parameter space and adjusting these variables based on dose constraints, the system achieves superior dose conformality compared to manual parameter selection, while the automation handles the complexity of coordinating all parameter changes.
2Productivity
If manual iteration of beam parameters is performed, then the device complexity is lower, but the productivity of treatment plan development is reduced
Solution Approach 1:
The treatment planning system performs self-optimization through automated algorithms. The system independently evaluates dose distributions, identifies suboptimal parameters, and iteratively adjusts beam parameters to satisfy dose constraints without requiring continuous manual intervention. This self-service capability accelerates treatment plan development while the automated infrastructure manages the computational complexity.
Solution Approach 2:
The system implements feedback loops where dose distribution results are continuously evaluated against clinical constraints, and beam parameters are adjusted based on this feedback. The automated optimization algorithm uses dose calculation feedback to guide parameter adjustments, enabling rapid convergence to optimal treatment plans. This feedback-driven automation increases productivity while systematically managing the complexity of multiple interacting parameters.
3Manufacturing precision
If beam parameters are manually optimized, then the ease of operation is maintained, but the manufacturing precision for irregular tumor shapes is insufficient
Solution Approach 1:
By inverting the planning approach to start with dose constraints and work backward to determine optimal beam parameters, the system achieves superior precision for irregular tumor geometries. The automated inverse optimization algorithm can handle complex three-dimensional dose distribution requirements that are difficult to achieve through manual forward planning, while the system manages the operational complexity through algorithmic automation.
Data Source
AI summary
Systems and methods for automatically determining a beam parameter at each of a plurality of treatment nodes are disclosed. The beam parameter may include a beam shape, beam size and/or beam orientation. Systems and methods for automatically selecting multiple collimators in a radiation treatment system are also disclosed.