Radiation Therapy Image Comparison for Tissue Property Deviation
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Solution Overview
Problem
Radiation therapy systems face challenges in adapting to changes in tissue properties during treatment, as initial parameter settings may become less accurate due to changes in tissue geometry and properties over time, leading to potential incorrect dosing and irradiation geometry.
Innovation Solution
A method and apparatus that utilize automatic image comparison using CT images before and after treatment to detect deviations in tissue properties, generating a deviation signal and automatically adjusting radiation system parameters, with adjustable threshold values for sensitivity and enabling user control over parameter changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If initial parameter settings are made based on pre-treatment imaging, then treatment planning can be completed efficiently, but the parameter settings become inaccurate as tissue properties change during therapy
Solution Approach 1:
The system implements continuous feedback by acquiring images during radiation therapy treatment, comparing them with reference images from pre-treatment imaging, and automatically adjusting radiation parameters based on detected tissue property changes. This closed-loop feedback mechanism ensures parameter accuracy is maintained throughout the treatment course despite tissue changes.
Solution Approach 2:
The system performs preliminary actions by establishing reference images and parameter settings before treatment begins, then uses these as a baseline for continuous monitoring and adjustment. The reference data is prepared in advance to enable rapid comparison and adaptation during therapy.
2Adaptability or versatility
If manual monitoring and adjustment of radiation parameters is performed, then flexibility in handling tissue changes is achieved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically detecting tissue property changes through image comparison and generating adjusted radiation parameters without requiring manual intervention. The automated parameter adjustment reduces time loss while maintaining adaptability to tissue changes throughout the treatment course.
Solution Approach 2:
Automatic feedback loops continuously monitor tissue properties and adjust parameters in real-time, eliminating the need for manual monitoring and adjustment. This automated feedback system maintains high adaptability while significantly reducing the time and operational complexity associated with manual processes.
3Measurement precision
If continuous image monitoring is implemented to detect tissue changes, then treatment accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from acquired images that indicate tissue property changes, rather than processing entire images. This extraction approach maintains high detection accuracy while reducing computational complexity and processing requirements.
Solution Approach 2:
The monitoring system focuses on local quality by examining specific regions and features within images that are most indicative of tissue changes, rather than analyzing entire images uniformly. This localized approach improves detection precision for critical areas while minimizing overall system complexity.
Data Source
AI summary
A method is provided for determining and monitoring parameters of a radiation treatment. The method comprises producing a first image of a region of a patient body to be radiated using a medical imaging process, determining a parameter setting of a radiation system using the first image, and providing the radiation treatment to the body region to be radiated using the radiation system with the determined parameter setting. The method further comprises producing a second image of the body region radiated with the parameter setting, automatically comparing the first image and the second image, and generating a deviation signal when a difference between the first image and the second image exceeds a predetermined threshold value.