Medical Display Processing for 2D-3D Image Superimposition
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Solution Overview
Problem
Existing methods face challenges in accurately and efficiently superimposing 2-dimensional image data from medical measurement equipment, such as ultrasonic diagnostic equipment, with 3-dimensional geometric models of the heart, due to differences in data formats and fluctuations in measured images, especially during ultrasonic diagnosis where strain and movement affect the positioning of cross-section images.
Innovation Solution
A display processing method and apparatus that perform transformation processing to align and superimpose cross-section images from medical equipment with 3-dimensional geometric models, using time-step adjustment, control point setting, and transformation processing to ensure accurate and efficient superimposition, allowing for expansion or reduction of data to synchronize image and model data intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If 2-dimensional image data from medical measurement equipment is superimposed with 3-dimensional geometric models, then the visualization of cardiac muscle behavior is improved, but the positioning accuracy deteriorates due to data format differences and image fluctuations
Solution Approach 1:
The patent introduces control points as intermediary elements that bridge the 2-dimensional image data and 3-dimensional geometric model. These control points are set on both the image and model to establish correspondence relationships, enabling accurate positioning despite format differences. The control points act as mediators that translate between different data representations and facilitate precise superimposition.
Solution Approach 2:
The patent transforms the 3-dimensional geometric model into 2-dimensional cross-section data that matches the format of the medical image data. This parameter transformation involves converting spatial coordinates and geometric properties from 3D to 2D, enabling direct comparison and superimposition while maintaining positioning accuracy despite the dimensional reduction.
2Loss of time
If cross-section images are superimposed over time, then the temporal analysis of cardiac behavior is improved, but the processing complexity increases due to the need for continuous alignment
Solution Approach 1:
The patent performs preliminary transformation processing on the 3-dimensional geometric model to pre-calculate and store 2-dimensional cross-section data at multiple time points. This preliminary action prepares the data in advance, so that during temporal analysis, only simple superimposition operations are needed rather than complex real-time transformations, significantly reducing processing complexity.
Solution Approach 2:
The patent divides the temporal analysis into discrete time steps, processing each time point independently. By segmenting the continuous temporal data into discrete frames and using control points to maintain correspondence between time steps, the system simplifies the overall processing complexity while preserving temporal analysis capabilities.
3Adaptability or versatility
If 3-dimensional geometric model data is transformed to 2-dimensional cross-section data, then the compatibility with medical image data is improved, but the information loss increases
Solution Approach 1:
The patent strategically reduces the 3-dimensional geometric model to 2-dimensional cross-sections at specific locations and orientations that are most relevant for cardiac analysis. By selecting critical cross-sections rather than complete 3D data, the system achieves compatibility with 2D medical images while minimizing information loss by preserving the most diagnostically important geometric features.
Solution Approach 2:
The patent applies different transformation strategies to different regions of the geometric model based on their diagnostic importance. Critical regions such as the myocardium boundaries and cardiac chambers receive higher fidelity transformation, while less critical areas use simplified approaches. This local quality differentiation maintains data compatibility while preserving essential information.
Data Source
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AI summary
A disclosed method includes: defining two first points in a model cross section of a model of an object and two corresponding second points in an image that is a cross section of the object for a reference time; performing first transforming including expansion or reduction for the model cross section so that a position of a second point is identical to a position of a corresponding first point; superimposing the image and the model cross section after the performing; second transforming a second model cross section for a second time after the reference time, so that positions of two second points in a second image for the second time are almost identical to positions of corresponding two first points in the second model cross section; and superimposing the second image and the second model cross section after the second transforming.