Co-expression Signatures for High-Dimensional Medical Imaging Analysis
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Solution Overview
Problem
Current methods for analyzing medical imaging data discard significant information by reducing high-dimensional data to lower-dimensional forms, preventing the utilization of comprehensive, pointwise data for quantitative personalized assessments of patient physiology and structure.
Innovation Solution
The generation of co-expression signature data that encodes local and global associations over the full data dimensionality, using similarity metrics to represent similarities or disparities between voxel pairs, allowing for precise analysis and comparison of physiological and structural changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If high-dimensional imaging data is reduced to lower-dimensional form for analysis, then data processing complexity is reduced, but significant information is discarded
Solution Approach 1:
The patent introduces co-expression signatures as an intermediary representation that bridges high-dimensional imaging data and clinical decision-making. Instead of directly reducing the high-dimensional data, the method computes pairwise similarities between data points to generate signature values that preserve essential information patterns while being computationally tractable for clinical use
Solution Approach 2:
The patent transforms the data representation by changing parameters from raw high-dimensional values to co-expression signature values derived from pairwise similarity metrics. This parameter transformation maintains the essential relationships in the data while reducing dimensionality in a information-preserving manner
2Loss of information
If high-dimensional data is used in its entire native form, then complete patient information is retained, but current analysis methods cannot utilize it
Solution Approach 1:
The patent segments the high-dimensional data analysis problem into manageable components by computing pairwise similarities between data points. This segmentation approach breaks down the complex high-dimensional analysis into numerous simple pairwise comparisons, each contributing to the overall co-expression signature
Solution Approach 2:
Co-expression signatures serve as an intermediary that makes high-dimensional data usable for clinical decision-making. The signatures translate complex multi-dimensional relationships into comparable metric values that can be used for patient assessment without requiring complex analysis infrastructure
3Quantity of substance
If dimensionality reduction is applied to imaging data, then data volume is reduced, but valuable information for decision-making is discarded
Solution Approach 1:
The patent changes the parameter representation from raw imaging data values to co-expression signature values based on pairwise similarities. This parameter change enables volume reduction while preserving the information content that is most relevant for clinical decision-making
Solution Approach 2:
The patent creates a copy of the essential information relationships through co-expression signatures. Instead of working with the full high-dimensional data, the method creates a simplified representation that copies the essential pairwise relationships, enabling efficient analysis without information loss
Data Source
AI summary
Described here are systems and methods for generating and analyzing co-expression signature data from scalar or multi-dimensional data fields contained in or otherwise derived from imaging data acquired with a medical imaging system. A similarity metric, such as an angular similarity metric, is computed between the data field components contained in pairs of voxels in the data field data. The data fields can be scalar fields, vector fields, tensor fields, or other higher-dimensional data fields. A probability distribution of these similarity metrics can be generated and used as co-expression signature data that indicate pairwise disparities in the data field data.