Deformable Registration Constraints for Adaptive Radiation Therapy
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Solution Overview
Problem
Current radiation therapy techniques face challenges in ensuring accurate delivery of treatment plans due to uncertainties such as patient setup errors, anatomical changes, and motion, which can lead to inconsistencies in radiation dose delivery, and existing quality assurance methods often fail to detect errors in input data, particularly in calibration issues.
Innovation Solution
Implementing an adaptive-type feedback loop for quality assurance that includes image-guided patient positioning, deformation-based dose recalculation, and delivery verification using deformable registration techniques to validate the treatment process and correct for errors, ensuring accurate radiation delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard radiation therapy with margins is used, then target coverage is ensured, but radiation exposure to healthy tissue increases
Solution Approach 1:
The patent implements a feedback mechanism by acquiring daily images during treatment, comparing them to the planning image through deformable registration, and using the deformation map to recalculate and adjust the treatment plan. This closed-loop feedback system allows the treatment to adapt to actual anatomical changes, enabling reduced margins while maintaining target coverage.
Solution Approach 2:
The treatment plan is made dynamic through online adaptation. Instead of using a static plan with fixed margins, the system continuously updates the treatment plan based on daily anatomical variations detected through image guidance and deformable registration, allowing margins to be optimized for each treatment session.
2Measurement precision
If deformable registration is used for image registration, then anatomical changes are captured, but computational complexity and time increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining anatomical landmarks and constraints before the deformable registration process. These pre-defined elements guide the deformation algorithm, reducing the computational search space and accelerating the registration process while maintaining precision in capturing anatomical changes.
Solution Approach 2:
The deformable registration is applied selectively to different regions of interest rather than uniformly across the entire image volume. By focusing computational resources on areas with significant anatomical changes and using local deformation models, the system achieves high measurement precision while reducing overall computational complexity.
3Manufacturing precision
If online adaptive therapy is implemented, then treatment accuracy is improved, but treatment time and resource requirements increase
Solution Approach 1:
The online adaptive therapy process is segmented into distinct modular steps: image acquisition, deformable registration, dose recalculation, and plan adaptation. Each module can be independently optimized and executed, allowing efficient resource allocation and parallel processing where possible, thereby maintaining high treatment accuracy while improving overall workflow efficiency.
Data Source
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AI summary
A system an method of placing constraints on a deformation map (figure 12) includes the acts of generating a deformation map (258) between two images, identifying a defined structure in one of the images (254), applying the deformation map to relate the defined structure from the one image onto the other image to create a deformation-based defined structure, modifying the deformation-based defined structure (262), and updating the deformation map in response to the step of modifying the deformation-based defined structure (266).