X-Ray Image Registration Using Rigid-Element Homography Separation
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Solution Overview
Problem
Existing imaging systems struggle to accurately register and quantify changes in patient anatomy over time, particularly after medical implant placement, due to changes in camera pose, patient position, and non-rigid transformations of the anatomy, leading to inaccurate comparisons of images taken at different times.
Innovation Solution
A method and system that utilize homography transformations for each rigid element in the anatomy, clustering these transformations to account for changes in camera pose and individual element pose, and registering images based on these transformations, while filtering out noise and non-rigid transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image registration methods are used to compare images taken at different times, then the registration process is simple, but the accuracy of anatomical change measurement deteriorates due to camera pose changes and patient position variations
Solution Approach 1:
The patent segments the anatomy into multiple rigid elements (vertebrae, implants) that can be individually tracked. Each rigid element is identified and tracked separately across time-separated images, allowing the system to distinguish between camera-induced transformations and actual anatomical changes. This segmentation approach resolves the contradiction by improving measurement precision through element-specific analysis while managing complexity through automated processing of individual segments.
Solution Approach 2:
The patent transforms the registration problem from a global image alignment task to a set of local rigid element transformations. By calculating transformation parameters (homographies) for each rigid element individually and then clustering these transformations, the system separates camera pose changes from anatomical changes. This parameter-based approach improves measurement accuracy while the clustering algorithm manages computational complexity.
2Measurement precision
If homography transformations are calculated for each rigid element to account for camera pose changes, then the accuracy of anatomical change detection improves, but the computational complexity increases
Solution Approach 1:
The patent merges the individual homography transformations of multiple rigid elements through clustering. By grouping similar transformations together and identifying the common portion attributable to camera pose changes, the system consolidates multiple complex calculations into a unified transformation model. This merging approach maintains high transformation accuracy while reducing computational complexity through shared processing.
Solution Approach 2:
The patent creates a virtual copy of the anatomy in a standardized coordinate system by applying calculated homography transformations. This virtual model allows for accurate comparison of anatomical changes over time without requiring repeated complex transformations of the original images. The copying approach preserves measurement precision while simplifying subsequent analysis.
3Reliability
If multiple rigid elements are tracked individually to separate camera pose changes from anatomical changes, then the reliability of long-term anatomical monitoring improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements automated identification and tracking of rigid elements using image processing algorithms that automatically detect vertebrae and implants in X-ray images. The system self-services by eliminating manual annotation requirements, using computational methods to identify rigid elements based on their geometric and radiographic characteristics. This approach improves long-term monitoring reliability through consistent automated detection while reducing the difficulty of element identification.
Solution Approach 2:
The patent employs iterative refinement of rigid element identification through feedback from transformation analysis. As the system calculates homographies and clusters transformations, it uses this information to refine element detection and separation. The feedback loop continuously improves element identification accuracy, enhancing monitoring reliability while managing detection difficulty through adaptive processing.
Data Source
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AI summary
A method according to one embodiment of the present disclosure comprises receiving a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements; receiving a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements; determining a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; calculating a homography for each transformation in the set of transformations to yield a set of homographies; and identifying, using the set of homographies, a common portion of each transformation attributable to a change in camera pose, and an individual portion of each transformation attributable to a change in rigid element pose.