Normative Reference Models for Real-Time High-Resolution Imaging
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Solution Overview
Problem
Existing normative modeling approaches for medical imaging are limited by computational cost, resolution, and data availability, failing to capture high-resolution anatomical or functional features and requiring re-computation for new regions of interest, which is slow and often inaccessible.
Innovation Solution
A method using graph signal processing to generate high-resolution normative charts by constructing normative basis and cross-basis models from medical scans, enabling efficient estimation of deviations from healthy norms across the human lifespan, allowing real-time computation of normative charts for arbitrary regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution normative models are computed using original data banks, then measurement precision is improved, but computational cost increases and device complexity increases
Solution Approach 1:
The patent creates a compressed normative model representation that captures essential spatial relationships without storing the complete original data. This allows high-resolution queries to be answered through the compressed model rather than reprocessing the full data bank, reducing computational cost while maintaining measurement precision.
Solution Approach 2:
The patent extracts and stores only the essential spatial relationship information from the original data bank in a compressed normative model. This extraction allows the system to answer high-resolution queries efficiently without needing to access or reprocess the complete original data, thereby reducing computational complexity.
2Device complexity
If normative models are limited to coarse measurements, then computational cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements a dynamic query system that can adapt the resolution of normative models based on the specific spatial query requirements. The compressed model allows the system to retrieve high-resolution information only when needed, rather than always maintaining high resolution, thus balancing computational cost with measurement precision dynamically.
3Productivity
If normative charts are computed for fixed regions, then productivity is improved, but adaptability deteriorates
Solution Approach 1:
The compressed normative model serves multiple functions: it can answer queries for any arbitrary spatial region, support different resolution requirements, and adapt to various imaging modalities. This universal representation allows the system to maintain high productivity while achieving broad adaptability without requiring separate computations for different regions.
4Adaptability or versatility
If re-computation is performed for new regions of interest, then adaptability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary computation of the compressed normative model once from the original data bank. This pre-computed model can then be quickly queried for any new region of interest without requiring re-computation, significantly reducing the time loss while maintaining adaptability to various spatial queries.
Data Source
AI summary
A system for generating a normative reference model is described. The system constructs spatial basis sets from medical scans, each basis set characterizing spatial property across the body part. A normative basis model is then generated for each spatial basis set, and a normative cross-basis model is generated from statistical relationships between the spatial basis sets. Thereafter, a normative reference model is generated from the normative basis models and the normative cross-basis model.