Focus-Adaptive Spine Image Alignment via Weighted Correlation
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Solution Overview
Problem
Current methods for constructing composite 3D volumes from MR images of the spine often result in poor alignment due to image distortions, leading to misalignment of the spinal area, which is critical for clinical diagnosis, as they favor matching large homogeneous areas over the spinal region.
Innovation Solution
A focus-adaptive method is introduced, where a weighting function is applied to digital images to prioritize the spinal area during alignment, suppressing peripheral regions and enhancing the weight on the center where the spine is located, using functions like Gaussian distributions to improve alignment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cross-correlation techniques are used to align images by matching large homogeneous areas, then alignment speed is improved, but alignment precision of the spinal region deteriorates
Solution Approach 1:
The patent applies different weighting factors to different regions of the image, assigning higher weights to the spinal region and lower weights to peripheral homogeneous areas. This local differentiation allows the alignment algorithm to prioritize precision in the critical spinal region while still utilizing the broader image context, thereby resolving the contradiction between speed and precision.
Solution Approach 2:
The patent modifies the correlation function by introducing region-specific weighting parameters. Instead of using uniform weighting across the entire image, the correlation calculation incorporates variable weights that emphasize the spinal region. This parameter change enables the system to maintain computational efficiency while achieving superior alignment precision in the diagnostically critical area.
2Device complexity
If uniform weighting is applied across the entire image during alignment, then processing simplicity is maintained, but alignment accuracy of critical regions deteriorates
Solution Approach 1:
The patent introduces spatially varying weighting factors that assign different importance to different regions of the image. The spinal region receives higher weights while peripheral areas receive lower weights. This local quality differentiation maintains reasonable processing complexity while dramatically improving alignment accuracy in the critical spinal region.
Solution Approach 2:
The patent modifies the alignment algorithm by changing the weighting parameter from uniform to spatially variable. The weighting function is designed to be computationally efficient while providing region-specific emphasis. This parameter change enables the system to achieve high alignment accuracy in critical regions without excessive increase in processing complexity.
3Loss of information
If peripheral regions are included with equal weight in alignment, then use of available image data is maximized, but alignment precision of the spinal area deteriorates due to distortions
Solution Approach 1:
The patent applies differential weighting where the spinal region is assigned higher weights and peripheral distorted regions are assigned lower weights. This approach preserves and utilizes data from the entire image while reducing the negative impact of distorted peripheral areas on the alignment precision of the critical spinal region.
Solution Approach 2:
The patent introduces a weighting parameter that varies spatially across the image. The weighting function is designed to down-weight peripheral regions that are prone to distortion while maintaining or emphasizing the spinal region. This parameter modification allows the system to utilize available image data effectively while protecting against distortion-induced alignment errors.
Data Source
AI summary
A method for aligning a pair of digital images includes providing a pair of digital images, wherein each said image comprises a plurality of intensities corresponding to a domain of points in a D-dimensional space, and the pair of images present adjacent views of a same object of interest. A weighting function is applied to each image of the pair of images, wherein the weighting function is centered on the object of interest, the weighting function has a maximum value on the object of interest, and the value of the weighting function decreases with increasing distance from the object of interest. The pair of images is aligned by correlated the weighted intensities on one image with those in the other image.