Multi-Modal Medical Image Registration and Visualization System
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Solution Overview
Problem
Current medical imaging systems face challenges in efficiently comparing and analyzing changes in tumors across different timepoints and modalities, lacking support for multiple dataset combinations and quick, accurate alignment of images from various sources.
Innovation Solution
A system and method for multi-modal visualization that allows the selection, loading, registration, and display of image datasets from CT, PET, SPECT, MRI, and ultrasound modalities at different timepoints, enabling the comparison of pre-therapy, ongoing, and post-therapy studies through automatic, landmark, or visual registration, with features like volume of interest quantification and maximum intensity projection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple medical imaging datasets from different modalities and timepoints are combined for comprehensive analysis, then the diagnostic information completeness is improved, but the system complexity and difficulty of image alignment increase
Solution Approach 1:
The system segments the complex multi-modal image analysis into distinct functional modules: image loading module, registration module, visualization module, and comparison module. Each module handles a specific aspect of the workflow, making the overall system more manageable and maintainable while supporting multiple datasets from different modalities and timepoints
Solution Approach 2:
The patent introduces a standardized intermediate representation layer that converts different medical imaging modalities (CT, MRI, PET, SPECT) into a common format for processing. This intermediary layer simplifies the complexity of handling diverse file structures and enables seamless integration of multiple datasets without requiring complex modality-specific processing logic
2Loss of information
If multiple medical imaging datasets from different modalities and timepoints are combined for comprehensive analysis, then the diagnostic information completeness is improved, but the difficulty of image alignment and registration increases
Solution Approach 1:
The system performs preliminary actions by automatically pre-aligning images using metadata information (patient positioning, scan parameters) before the actual registration process. This preliminary alignment significantly reduces the complexity of subsequent registration operations, enabling accurate comparison of images from different timepoints and modalities
Solution Approach 2:
The patent implements feedback mechanisms where the registration process continuously evaluates alignment quality using image similarity metrics and automatically adjusts transformation parameters. This iterative feedback loop ensures accurate registration even when dealing with complex multi-modal datasets, reducing the difficulty of image alignment
3Measurement precision
If comprehensive multi-modal image analysis is performed to improve diagnostic accuracy, then the detection precision is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary processing of images including normalization, noise reduction, and feature extraction before the actual diagnostic analysis. This preliminary action prepares the data in advance, enabling faster and more accurate comparison when multiple datasets are being analyzed, thus reducing the overall analysis time while maintaining high detection precision
Solution Approach 2:
The patent extracts and focuses on the most diagnostically relevant features from each image modality, separating essential information from redundant data. This extraction process reduces the amount of data that needs to be processed during comparison, thereby decreasing analysis time while preserving the precision needed for accurate tumor detection and monitoring
Data Source
AI summary
A system and method for monitoring disease progression or response to therapy using multi-modal visualization are provided. The method comprises: selecting a first image dataset of a first timepoint; loading the first image dataset of the first timepoint; selecting a second image dataset of a second timepoint; loading the second image dataset of the second timepoint; registering the first image dataset of the first timepoint and the second image dataset of the second timepoint; and displaying the first image dataset of the first timepoint and the second image dataset of the second timepoint.