Radiotherapy Plan Modulation Control for Dose Conformity and Motion Robustness
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Solution Overview
Problem
Existing radiotherapy planning systems face challenges in accurately adapting to anatomical changes due to physiological regression and patient movement, leading to underdosing of tumors and overdosing of surrounding organs, with traditional optimization methods being time-consuming and computationally intensive, and lacking flexibility in balancing dose conformity and field modulation.
Innovation Solution
A hybrid optimization framework integrating multi-field and single-field optimization techniques, with user-definable modulation parameters and interactive visualization, allows for dynamic control over field modulation and dose distribution, providing real-time feedback for iterative plan refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multi-field optimization is used to achieve precise dose delivery to target volume, then dose conformity is improved, but field modulation increases making the system more sensitive to patient movement
Solution Approach 1:
The patent implements a hybrid optimization framework that dynamically adjusts the balance between multi-field and single-field optimization techniques. The system uses iterative optimization with adjustable weighting factors that allow transition between highly modulated (multi-field) and less modulated (single-field) approaches, enabling adaptive control of field modulation levels based on treatment requirements and patient stability.
Solution Approach 2:
The patent changes optimization parameters by introducing a hybrid objective function that combines multi-field and single-field optimization objectives with adjustable weighting. This allows continuous adjustment of field modulation intensity through parameter tuning, balancing dose conformity against robustness to patient movement without requiring complete re-optimization.
2Manufacturing precision
If frequent updates to radiotherapy treatment plan are performed to account for anatomical changes, then treatment accuracy is improved, but treatment delivery is delayed due to complex procedures
Solution Approach 1:
The patent applies partial optimization by focusing computational efforts on the most critical aspects of plan adaptation rather than complete re-optimization. The hybrid framework performs selective updates to field modulation parameters based on detected anatomical changes, achieving sufficient treatment accuracy without the full computational burden of complete replanning.
Solution Approach 2:
The system performs preliminary optimization by pre-calculating robust treatment plans that account for expected anatomical variations. This preliminary robust optimization reduces the need for frequent complete replanning, as the initial plan is designed to maintain effectiveness across a range of anatomical configurations.
3Ease of manufacture
If conventional plan optimizers use static dosimetric parameters hardcoded in software, then optimization is simplified, but the process becomes time-consuming and computationally intensive when revisions are needed
Solution Approach 1:
The patent transforms static dosimetric parameters into dynamic, adjustable variables within the hybrid optimization framework. The system allows real-time modification of optimization weights and parameters without requiring software code changes, enabling flexible adaptation to different clinical scenarios and improving plan revision efficiency.
4Adaptability or versatility
If field modulation is reduced to improve robustness to patient movement, then sensitivity to movement is decreased, but target dose conformity is compromised
Solution Approach 1:
The hybrid optimization framework dynamically adjusts field modulation levels based on the relative importance of dose conformity versus robustness. By varying the weighting between multi-field and single-field optimization objectives, the system can adaptively find the optimal balance for each specific treatment case and patient condition.
Data Source
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AI summary
Disclosed herein are methods (200) and systems (100) using artificial intelligence models (420) to control the amount of modulation in radiotherapy plans (440). The system (100) may receive (210) parameters defining a plan objective for a radiotherapy treatment plan (440). The system (100) may establish (220) dose distribution parameters for each field of the plan objective. The system (100) may initialize (230) user-defined field regularization factors for each field of the plan objective. The system (100) may initialize (240) plan regularization factors. In response to applying the user-defined field regularization factor to a corresponding field of the plan objective and the plan regularization factor to the plan objective, the system (100) may generate (250) a treatment plan objective incorporating weighted components for the plan objective and each field. The system (100) may transmit (260) the treatment plan objective to a radiotherapy treatment planning computer model. The radiotherapy treatment planning computer model may determine the radiotherapy treatment plan (440) based on the treatment plan objective.