Winston-Lutz Test Image Processing for Isocenter Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The Winston-Lutz test for radiation isocenter alignment in radiation oncology treatment systems faces challenges due to beam scattering and penumbra effects, leading to uncertainty in image edge detection and requiring high-resolution imaging, which is susceptible to noise and manual estimation errors.
Innovation Solution
An image processing method that converts images using a polar-to-rectangular coordinate scheme to identify the center of a ball and cone images, calculating standard deviation values to determine a score representing the accuracy of the center estimation, and determining eccentricity between the ball and cone centers to improve alignment precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Winston-Lutz test is performed using conventional film or digital imager, then the radiation isocenter can be found, but beam scattering and penumbra effects cause blurring of the image edge resulting in uncertainty of the true edge and calculated eccentricity
Solution Approach 1:
The patent replaces manual visual estimation methods with an automated computer-based image processing system. The processor automatically detects ball image centers, calculates eccentricity, and determines isocenter positioning without human intervention, eliminating subjective errors and improving measurement consistency and reliability.
Solution Approach 2:
The patent uses digital imaging to create a copy of the physical ball and cone setup, allowing multiple measurements and analyses of the same object without physical manipulation. This digital replica enables repeated automated analysis to verify results and improve measurement reliability.
2Measurement precision
If high-resolution film or digital imager is used to accurately find the edge of the ball image, then measurement precision improves, but the system becomes susceptible to random noise and requires manual estimation
Solution Approach 1:
The patent replaces complex manual estimation procedures with a simplified automated algorithm. The processor automatically identifies the ball image center by analyzing pixel intensity distributions and calculating geometric centers, eliminating the need for manual ruler measurements and reducing the impact of noise through computational methods.
Solution Approach 2:
The image processing system performs self-calibration and automatic center detection without requiring external reference markers or manual intervention. The algorithm independently identifies ball and cone centers, calculates their eccentricity, and determines isocenter alignment status autonomously.
3Ease of operation
If visual estimation of the image edge is performed manually, then the process is simple, but random noise in the image and manual errors reduce measurement accuracy
Solution Approach 1:
The patent replaces manual visual estimation with an automated computer-based image processing system that calculates the precise center of the ball image by analyzing the distribution of pixel intensities. This automated approach eliminates human error while maintaining operational simplicity through software automation.
Solution Approach 2:
The system incorporates iterative refinement where the processor initially estimates ball and cone centers, then uses these estimates to guide more precise measurements. The calculated eccentricity feedback is used to determine whether alignment corrections are needed, creating a closed-loop measurement system that improves precision through iterative validation.
Data Source
AI summary
An image processing method includes: obtaining an image that includes a ball image and a cone image; obtaining an estimate of a center of the ball image; converting the image to a converted image using a processor based at least in part on the estimate of the center of the ball image, wherein the converted image comprises a converted ball image that looks different from the ball image in the image, and a converted cone image that looks different from the cone image in the image; identifying the converted ball image in the converted image; and analyzing the converted ball image to determine a score that represents an accuracy of the estimate of the center of the ball image.