Localized Image Registration and Fusion for Medical Imaging
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Solution Overview
Problem
Current medical imaging techniques struggle to effectively combine images from disparate modalities, especially when resolution and noise characteristics differ, leading to suboptimal information delivery and interpretation challenges in clinical applications such as image-assisted biopsy and treatment planning.
Innovation Solution
The system allows for the registration and fusion of selected Regions-of-Interest (ROIs) or Volumes-of-Interest (VOIs) from different imaging devices, enabling the creation of a unified image with improved utility, reducing processing time and enhancing clinical results by overlaying or blending relevant image data without requiring entire image volume registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If entire image volumes from different modalities are fused using constant coefficients, then comprehensive image coverage is achieved, but image interpretability deteriorates when resolution and noise characteristics differ
Solution Approach 1:
The patent divides the image fusion process into two distinct segments: (1) fusion of entire image volumes using constant coefficients for comprehensive coverage, and (2) fusion of selected regions-of-interest using variable coefficients for enhanced interpretability. This segmentation allows each region to be processed according to its specific requirements, resolving the contradiction between comprehensive coverage and information interpretability.
Solution Approach 2:
The patent applies different fusion coefficients to different regions of the image based on their specific characteristics. For regions with similar resolution and noise characteristics, constant coefficients are used. For regions with differing characteristics (such as functional vs. anatomical imaging), variable coefficients are applied to optimize the fusion quality and interpretability of each local region.
2Loss of information
If entire image volumes are co-registered and fused, then complete anatomical and functional information is combined, but processing time increases
Solution Approach 1:
The patent segments the image processing workflow into two phases: initial fusion of entire image volumes to establish comprehensive coverage and alignment, followed by selective fusion of regions-of-interest that require detailed analysis. This segmentation reduces the total processing time by avoiding redundant computation on all image data while maintaining information completeness for critical regions.
Solution Approach 2:
The patent applies partial action by performing detailed variable coefficient fusion only on selected regions-of-interest rather than entire image volumes. The initial constant coefficient fusion provides sufficient coverage for alignment purposes, allowing subsequent processing to focus computational resources on specific areas requiring enhanced detail, thus reducing overall processing time while maintaining necessary information quality.
3Loss of information
If variable coefficients are used for region-specific fusion, then information interpretability improves, but device complexity increases
Solution Approach 1:
The patent segments the fusion algorithm into two distinct modes: constant coefficient fusion for general image coverage and variable coefficient fusion for specific regions-of-interest. This segmentation allows the system to use the simpler constant coefficient method for most of the image, reserving the more complex variable coefficient method only for regions where enhanced interpretability is necessary, thus balancing information quality with algorithmic simplicity.
Solution Approach 2:
The patent implements local quality by applying variable coefficients only to specific regions where resolution and noise characteristics differ significantly, rather than across the entire image. This localized application of complex algorithms reduces the overall computational burden and algorithmic complexity while maintaining high interpretability where it is most needed.
4Device complexity
If constant coefficients are used for image fusion, then algorithm simplicity is maintained, but diagnostic accuracy deteriorates for regions with different noise characteristics
Solution Approach 1:
The patent segments the image processing into two distinct operations: initial fusion using simple constant coefficients for overall image alignment and coverage, followed by selective refinement of regions-of-interest using variable coefficients to enhance diagnostic accuracy. This segmentation allows the system to maintain algorithmic simplicity for the majority of processing while applying enhanced methods only where diagnostic precision is critical.
Solution Approach 2:
The patent applies partial action by using variable coefficients selectively only for regions where diagnostic accuracy is most critical, such as areas with differing noise characteristics or clinical significance. The constant coefficient method suffices for regions where diagnostic precision requirements are lower, thus maintaining overall algorithmic simplicity while improving accuracy where it matters most.
Data Source
AI summary
Systems and methods are described for co-registering, displaying and quantifying images from numerous different medical modalities, such as CT, MRI and SPECT. In this novel approach co-registration and image fusion is based on multiple user-defined Regions-of-Interest (ROI), which may be subsets of entire image volumes, from multiple modalities, where the each ROI may depict data from different image modalities. The user-selected ROI of a first image modality may be superposed over or blended with the corresponding ROI of a second image modality, and the entire second image may be displayed with either the superposed or blended ROI.