Adaptive Radiation Therapy Plan Update
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Solution Overview
Problem
Radiation therapy faces challenges in accurately delivering radiation to cancerous tissues while minimizing damage to healthy tissues due to changes in the size, shape, orientation, or position of tumors and organs at risk over the course of multiple treatment sessions.
Innovation Solution
A method and system that involve acquiring treatment images during each session, updating initial feature contours, and optimizing radiation treatment parameters based on these updates to adapt the treatment plan dynamically, ensuring precise delivery of radiation to malignant tissue while minimizing exposure to nearby organs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pre-selected radiation treatment plan is used for multiple treatment sessions, then treatment consistency and workflow efficiency are improved, but accuracy of radiation delivery deteriorates due to changes in tumor or organ position, size, or shape over time
Solution Approach 1:
The treatment plan parameters are made dynamic by allowing automatic adjustment based on treatment images acquired during each session. The system transitions from a static pre-selected plan to a dynamic adaptive plan that automatically updates parameters such as beam angles, intensities, and field shapes based on actual tumor and organ positions observed in treatment images, thereby maintaining accuracy while preserving workflow efficiency.
Solution Approach 2:
The system implements feedback by acquiring treatment images during each radiation therapy session and using these images to automatically adjust treatment plan parameters. The feedback loop compares the actual anatomical structures observed in treatment images with the original treatment plan, and automatically modifies parameters to account for any deviations in tumor or organ position, size, or shape, ensuring accurate radiation delivery without requiring manual plan reconfiguration.
2Manufacturing precision
If treatment plan parameters are manually adjusted for each treatment session based on treatment images, then accuracy of radiation delivery is improved, but workflow efficiency and productivity deteriorate due to time-consuming manual re-planning
Solution Approach 1:
The system enables self-service by automatically adjusting treatment plan parameters based on treatment images without requiring manual intervention from radiation therapists or physicists. The automatic parameter adjustment system processes treatment images, identifies changes in tumor or organ characteristics, and autonomously modifies treatment parameters according to pre-defined optimization criteria, thereby maintaining high accuracy while preserving workflow efficiency.
Solution Approach 2:
The system implements parameter changes by automatically modifying treatment plan parameters such as beam angles, intensities, and field shapes based on observed changes in tumor or organ position, size, or shape from treatment images. The automatic parameter adjustment mechanism dynamically updates these parameters within the treatment plan to account for anatomical variations, ensuring accurate radiation delivery without the time-consuming manual re-planning process.
3Measurement precision
If patient alignment is performed to realign the subject with the malignant tissue in the isocenter, then overall patient positioning accuracy is improved, but relative position changes between malignant tissue and organs at risk cannot be addressed
Solution Approach 1:
The system applies local quality by focusing on the specific region of interest (tumor and surrounding organs) rather than just overall patient alignment. Treatment images are analyzed to detect local position, size, or shape changes of the tumor and organs at risk, and treatment parameters are adjusted specifically for these local anatomical variations, thereby addressing relative positioning accuracy between malignant tissue and organs at risk while maintaining overall patient positioning accuracy.
Data Source
AI summary
In a radiation therapy method, one or more planning images are acquired (102) of a subject. Features of at least malignant tissue are contoured in the one or more planning images to produce one or more initial feature contours. One or more treatment images of the subject are acquired (114). The one or more initial feature contours are updated (122) based on the one or more treatment images. Radiation treatment parameters are optimized (126) based upon the updated one or more feature contours. Radiation treatment of the subject is performed (130) using the optimized parameters.