Adaptive Radiotherapy Decision Console Using Perturbation Models
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Solution Overview
Problem
Current radiation therapy approaches face delays and inefficiencies in determining whether adaptive radiotherapy (ART) is necessary, as the decision often requires substantial computational resources and time, leading to potential sub-optimal treatment delivery due to the need for image data transfer and re-contouring, which can result in delays and increased workload for radiation physicists.
Innovation Solution
Implementing a console-based system that determines perturbations between current and planning images using a radiation therapy plan-specific perturbation model, allowing for rapid ART recommendations without simulating dose distributions, enabling immediate decision-making at the linac console and reducing the need for TPS consultation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adaptive radiotherapy decision-making is performed using traditional methods (image data transfer and re-contouring), then treatment accuracy is improved, but treatment delivery time increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis by pre-defining perturbation models and thresholds for various anatomical structures before treatment delivery. When a CBCT image is acquired, the system compares it against these pre-established models to rapidly determine if ART is needed, eliminating the need for time-consuming re-contouring while maintaining treatment accuracy
Solution Approach 2:
The system creates simplified representations (perturbation models) of complex anatomical structures and their expected variations. These models serve as proxies that can be quickly compared against actual CBCT images to assess whether anatomical changes warrant adaptive re-planning, thus avoiding full re-contouring while preserving essential accuracy
2Manufacturing precision
If adaptive radiotherapy decision-making is performed using traditional methods (image data transfer and re-contouring), then treatment accuracy is improved, but time consumption increases and loss of time worsens
Solution Approach 1:
The system performs preliminary analysis by pre-defining perturbation models and thresholds for various anatomical structures before treatment delivery. When a CBCT image is acquired, the system compares it against these pre-established models to rapidly determine if ART is needed, eliminating the need for time-consuming re-contouring while maintaining treatment accuracy
Solution Approach 2:
The system skips the time-consuming steps of full image re-contouring and manual review by using automated perturbation detection algorithms. The system rushes through the decision-making process by comparing CBCT images against pre-defined thresholds and models, providing rapid ART recommendations without sacrificing treatment accuracy
3Manufacturing precision
If adaptive radiotherapy decision-making is performed using traditional methods, then treatment accuracy is improved, but device complexity and operational complexity increase
Solution Approach 1:
The system extracts only the essential information needed for ART decision-making by comparing CBCT images against pre-defined perturbation models for critical anatomical structures. This extraction approach avoids the complexity of full re-contouring while capturing the most relevant anatomical changes that would impact treatment accuracy
4Manufacturing precision
If adaptive radiotherapy decision-making is performed using traditional methods, then treatment accuracy is improved, but workload for radiation physicists increases
Solution Approach 1:
The system performs self-service by automatically comparing CBCT images against pre-defined perturbation models and generating ART recommendations without requiring radiation physicist intervention for routine assessments. This automation maintains treatment accuracy while significantly reducing the operational workload and time commitment required from physicists
Data Source
AI summary
A radiation therapy delivery device console (50) controls a radiation therapy delivery device (36) and an imaging device (40, 42), and further performs adaptive radiotherapy (ART) recommendation as follows. The imaging device is controlled to acquire a current image (44) of a patient. At least one perturbation of the current image is determined compared with a radiation therapy planning image (1) from which a radiation therapy plan (22) for the patient has been generated. An ART recommendation score is computed, indicating whether ART should be performed, based on the determined at least one perturbation. A recommendation is displayed as to whether ART should be performed based on the computed ART recommendation score, or an alarm is displayed conditional upon the computed ART recommendation score satisfying an ART recommendation criterion.