Hierarchical Analytics Framework for Medical Imaging Data Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging technologies face challenges in effectively utilizing increasing spatial and temporal resolution, leading to overwhelming data that clinicians struggle to interpret objectively and quantitatively, which is essential for precise disease detection, characterization, and treatment monitoring.
Innovation Solution
The implementation of a hierarchical analytics framework that combines computerized image analysis and data fusion algorithms with patient clinical chemistry and blood biomarker data to provide a multi-factorial panel for distinguishing between different disease subtypes, enabling more accurate and objective phenotyping and risk stratification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enhanced imaging techniques with higher spatial and temporal resolution are used, then the ability to detect and characterize disease improves, but the complexity and volume of data increase making it difficult for clinicians to interpret
Solution Approach 1:
The patent introduces computerized image analysis algorithms and data fusion algorithms as intermediaries between the imaging system and clinicians. These algorithms automatically process, analyze, and integrate complex imaging data, transforming raw high-resolution images into interpretable quantitative results and visualizations that clinicians can easily understand and use for decision-making.
Solution Approach 2:
The patent segments the complex imaging data into organized components through hierarchical analytics frameworks. The system divides medical images into multiple levels of analysis (anatomical structures, tissue characteristics, pathological features) and processes each segment separately, making the overall complex data manageable and interpretable while maintaining high measurement precision.
2Measurement precision
If quantitative imaging analysis is performed to objectively characterize disease, then diagnostic accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing imaging data through automated segmentation and feature extraction algorithms before clinical interpretation is needed. The system pre-computes quantitative metrics, generates preliminary diagnostic assessments, and prepares organized data structures in advance, reducing the time required for final diagnostic accuracy assessment while maintaining high measurement precision.
3Adaptability or versatility
If multiple imaging modalities and contrast agents are used to assess different tissue characteristics, then the comprehensiveness of disease characterization improves, but the cost and complexity of the imaging protocol increase
Solution Approach 1:
The patent merges data from multiple imaging modalities and contrast agent enhancements into a unified quantitative analysis framework. The data fusion algorithms integrate information from different imaging sequences and modalities, combining them into comprehensive disease characterizations that assess multiple tissue characteristics simultaneously, reducing protocol complexity while maintaining comprehensive assessment capability.
Data Source
AI summary
Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.