Radiation Therapy Plan Optimization Using Sensitivity Matrices
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Solution Overview
Problem
Current radiation-therapy treatment plans are computationally intensive and costly due to their inability to effectively discriminate between target and adjacent tissues, leading to inefficiencies in optimizing radiation delivery and potential exposure of non-target structures.
Innovation Solution
The method involves accessing target and non-target information, including spatial uncertainties, to characterize radiation-therapy treatment plan optimization considerations, which influences the optimization process to prioritize certain radiation-beam directionalities and time frames, thereby reducing exposure risks and improving plan efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If typical optimization processes are used for radiation therapy treatment plans, then treatment plan quality can be improved, but computational cost and processing time increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing sensitivity matrices and gradient information about how radiation dose distributes in different anatomical structures. This preliminary analysis allows the optimization process to start from an informed position rather than exploring all possible parameters from scratch, significantly reducing computation time while maintaining treatment plan quality
Solution Approach 2:
The invention changes the parameter space by transforming the optimization problem from directly optimizing radiation beam parameters to optimizing a reduced set of sensitivity-based parameters. By changing variables to represent dose sensitivity relationships rather than raw beam parameters, the system achieves faster convergence while preserving treatment plan effectiveness
2Manufacturing precision
If typical optimization processes are used for radiation therapy treatment plans, then treatment plan quality can be improved, but computational cost increases requiring expensive processing platforms
Solution Approach 1:
The system extracts and separates the computationally intensive components of treatment plan optimization into pre-calculable sensitivity analyses. By extracting the dose distribution sensitivity information and storing it in lookup tables, the system removes the need for expensive real-time computations during optimization, enabling standard platforms to handle complex treatment plans
Solution Approach 2:
The invention creates simplified copies or representations of the complex radiation transport physics through pre-computed sensitivity matrices and dose response models. These computational models serve as surrogates that capture the essential physics without requiring full Monte Carlo simulations or complex analytical calculations during optimization, reducing platform requirements while maintaining accuracy
3Reliability
If radiation is applied to treat tumors, then tumor elimination is achieved, but adjacent critical tissues may be exposed to harmful radiation
Solution Approach 1:
The system applies local quality by optimizing radiation dose distribution at different spatial locations independently. Through sensitivity-based optimization, the system can prescribe different dose levels to different anatomical structures (tumor vs. critical organs) based on their individual radiation sensitivity and clinical priorities, delivering high dose to tumor while limiting dose to critical tissues
Solution Approach 2:
The invention implements feedback mechanisms by continuously evaluating dose sensitivity to critical structures during optimization and adjusting beam parameters accordingly. The pre-computed sensitivity matrices provide real-time feedback on how parameter changes affect both tumor dose and critical organ dose, enabling the optimizer to navigate the solution space while respecting tissue constraints
Data Source
AI summary
These various embodiments access target information regarding a radiation-therapy treatment volume for a given patient as well as non-target information regarding at least one structure other than the radiation-therapy treatment volume that also comprises a part of the given patient. These embodiments then provide for accessing uncertainties information regarding spatial uncertainties as pertain to at least one of the target information and the non-target information and using that uncertainties information to characterize at least one radiation-therapy treatment plan optimization consideration with respect to a preference of usage to thereby provide preference considerations. These preference considerations are then used to influence a follow-on radiation-therapy treatment plan optimization process when developing a treatment plan for the radiation-therapy treatment volume.