Adaptive Radiation Therapy Optimization via Overlapping Imaging and Delivery
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Solution Overview
Problem
Current radiation therapy techniques are inefficient due to the lengthy time gap between imaging and radiation delivery, which allows target volume and healthy tissue characteristics to change, leading to suboptimal dose distribution and increased treatment duration, especially with on-line adaptive radiation therapy (ART) requiring prolonged hospital resource occupation.
Innovation Solution
A method involving fractional treatments where initial image data is used to optimize radiation treatment plans, with subsequent fractional image data allowing for rapid re-optimization of radiation delivery variables during treatment, enabling simultaneous or overlapping image acquisition, optimization, and delivery processes to adapt to changing tissue conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If on-line adaptive radiation therapy is implemented with separate imaging and optimization procedures for each fraction, then dose distribution accuracy is improved by adapting to tissue changes, but treatment time and hospital resource occupation increase significantly
Solution Approach 1:
The patent combines imaging, optimization, and radiation delivery into an integrated on-line adaptive radiation therapy system where these procedures are performed sequentially during the same treatment session, eliminating the need for separate imaging and optimization appointments while maintaining dose accuracy
Solution Approach 2:
The system performs imaging and optimization procedures in advance during the same treatment session before radiation delivery begins, ensuring that the most current anatomical information is used for dose calculation while minimizing the time between imaging and delivery
2Productivity
If traditional radiation therapy with weekly imaging and planning is used, then hospital resource occupation is reduced, but dose distribution becomes suboptimal due to tissue changes over time
Solution Approach 1:
The system implements real-time feedback by performing imaging during each treatment fraction to detect tissue changes, then using this information to update optimization parameters and adjust radiation delivery accordingly, ensuring dose accuracy adapts to current anatomical conditions
Solution Approach 2:
The system transitions from static weekly planning to dynamic on-line adaptation where imaging, optimization, and delivery parameters are continuously updated based on real-time anatomical changes detected during each treatment session
3Measurement precision
If imaging and optimization are performed separately before each radiation fraction, then computational accuracy is improved, but the time gap allows target volume and healthy tissue characteristics to change
Solution Approach 1:
The system maintains continuous adaptation by performing imaging, optimization, and delivery in an unbroken sequence during each treatment fraction, eliminating time gaps that would allow tissue characteristics to change and ensuring the most current anatomical data is always used for dose calculation
Data Source
AI summary
Methods and systems are disclosed for radiation treatment of a subject involving one or more fractional treatments. A fractional treatment comprises: obtaining fractional image data pertaining to a region of interest of the subject; performing a fractional optimization of a radiation treatment plan to determine optimized values of one or more radiation delivery variables based at least in part on the fractional image data; and delivering a fraction of the radiation treatment plan to the region of interest using the optimized values of the one or more radiation delivery variables as one or more corresponding parameters of the radiation treatment plan. A portion of performing the fractional optimization overlaps temporally with a portion of at least one of: obtaining the fractional image data and delivering the fraction of the radiation treatment plan.