Brachytherapy Seed Localization via Multi-View Image Segmentation
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Solution Overview
Problem
Current seed reconstruction methods in brachytherapy are limited by computational expense, sensitivity to pose errors, inability to resolve the hidden seed problem, requirement for large numbers of images and wide acquisition angles, and constrained motion of imaging devices, making them impractical for routine clinical use.
Innovation Solution
A method that processes data from three or more two-dimensional images to calculate the likelihood of correct pairings and triplet matchings, reducing computational complexity through cost-based pruning and dimensionality reduction, allowing for accurate three-dimensional reconstruction of radioactive seeds within the body.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current seed reconstruction methods are used, then three-dimensional reconstruction of radioactive seeds can be achieved, but computational expense becomes excessive and impractical for routine clinical use
Solution Approach 1:
The patent segments the reconstruction problem into multiple stages: first identifying seed candidates in individual images, then forming pairs from two images, triplets from three images, and finally quartets from four images. This hierarchical segmentation reduces the computational complexity by breaking down the exhaustive search problem into manageable sub-problems that can be solved incrementally.
Solution Approach 2:
The patent transitions from two-dimensional image data to three-dimensional reconstruction by utilizing multiple imaging angles. By acquiring images at different angles and systematically combining them (pairs, triplets, quartets), the method extracts depth information and reconstructs three-dimensional seed positions without requiring computationally intensive full-volume reconstruction.
2Measurement precision
If a large number of images with wide acquisition angles are used, then reconstruction accuracy improves, but device complexity and acquisition time increase
Solution Approach 1:
The patent employs a progressive approach where reconstruction accuracy is improved incrementally by adding more images (from pairs to triplets to quartets). This allows the system to achieve sufficient accuracy with a manageable number of images, avoiding the need for excessive imaging that would increase device complexity and acquisition time.
Solution Approach 2:
The method performs preliminary processing by identifying seed candidates in individual images before combining them into pairs, triplets, and quartets. This preliminary action reduces the search space for subsequent multi-image combinations, allowing accurate reconstruction with fewer images and simpler imaging geometry.
3Stability of the object's composition
If constrained motion of imaging devices is imposed, then reconstruction stability improves, but adaptability to different clinical scenarios decreases
Solution Approach 1:
The patent uses dynamic programming to systematically evaluate and combine seed matches across multiple images. This dynamic approach allows the system to adapt to varying imaging conditions and geometries while maintaining reconstruction stability through the systematic evaluation of all possible seed correspondences across different image combinations.
Solution Approach 2:
The method is designed to be universally applicable to different clinical scenarios by processing images from multiple angles and positions. The systematic combination of image pairs, triplets, and quartets allows the same algorithm to handle various imaging geometries and device positions without requiring constrained motion, thereby maintaining both stability and adaptability.
4Measurement precision
If the hidden seed problem is not addressed, then computational complexity remains low, but measurement precision deteriorates due to inability to locate all seeds
Solution Approach 1:
The patent addresses the hidden seed problem by segmenting the search process into hierarchical stages. By first identifying seeds in individual images, then forming pairs from two images, and progressively building up to triplets and quartets, the method systematically uncovers hidden seeds that may not be visible in any single image, improving localization completeness without overwhelming computational complexity.
Solution Approach 2:
The method uses intermediate results from lower-level combinations (pairs and triplets) as mediators to solve the hidden seed problem. By progressively building up from simpler image combinations to more complex ones, the system can identify seeds that are hidden in individual images but become visible when multiple images are combined, thereby improving measurement precision through systematic intermediate processing.
Data Source
AI summary
A method of processing image data from an imaging system for locating a plurality N of objects embedded in a body includes receiving data for a first two-dimensional image of a region of interest of the body containing the plurality N of objects, the first two-dimensional image being obtained from a first imaging setting of the imaging system relative to the region of interest; receiving data for a second two-dimensional image of a region of interest of the body containing the plurality N of objects, the second two-dimensional image being obtained from a second imaging setting of the imaging system relative to the region of interest; and receiving data for a third two-dimensional image of a region of interest of the body containing the plurality N of objects, the third two-dimensional image being obtained from a third imaging setting of said imaging system relative to said region of interest.