Phasic Classification Map for COPD Phenotype Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing Chronic Obstructive Pulmonary Disease (COPD) severity using computed tomography (CT) scans are limited in accurately identifying COPD phenotypes beyond emphysema and lack the ability to provide both global and local measures of disease severity, failing to effectively classify local variations in lung function and visualize COPD phenotypes.
Innovation Solution
The development of a phasic classification map (PCM) analysis technique that utilizes deformation registration of image data to compare images taken at different tissue states, allowing for voxel-by-voxel analysis and threshold analysis to segment regions indicating the presence or absence of conditions, enabling the classification of COPD phenotypes and monitoring disease status or response to therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative CT metrics are used to assess COPD severity, then global measures of disease severity can be obtained, but the ability to provide local measures and classify local variations in lung function is limited
Solution Approach 1:
The patent segments the lung into multiple local regions and applies separate analysis to each region. The system divides the lung volume into discrete zones and performs quantitative CT analysis on each segment independently, enabling local classification of COPD phenotypes while maintaining global context through hierarchical organization of results.
Solution Approach 2:
The patent adds a spatial dimension to traditional global CT metrics by mapping quantitative values to specific three-dimensional locations within the lung. This dimensional transformation enables visualization and classification of local variations in lung function, converting scalar global measures into spatially-resolved maps that preserve both local detail and global context.
2Measurement precision
If traditional CT analysis methods are used, then the system is simple to operate, but the accuracy in identifying COPD phenotypes beyond emphysema is limited
Solution Approach 1:
The system performs automated classification of COPD phenotypes without requiring manual intervention. The quantitative CT analysis pipeline automatically segments lung regions, calculates metrics, and classifies tissue phenotypes based on predetermined criteria, eliminating the need for expert manual interpretation while maintaining high accuracy in identifying emphysema, fibrosis, and other COPD manifestations.
Solution Approach 2:
The patent employs multiple quantitative parameters beyond traditional single-metric analysis. By calculating and integrating multiple CT-derived parameters (density, texture, ventilation metrics) simultaneously, the system achieves superior phenotype identification accuracy. This multi-parameter approach transforms the analysis from simple visual assessment to comprehensive quantitative evaluation.
3Measurement precision
If visual inspection of CT scans is used, then the method is simple and quick, but the precision in detecting tissue changes over time is insufficient
Solution Approach 1:
The patent replaces manual visual inspection with automated computational analysis. The system uses computer-executed algorithms to perform quantitative CT analysis, automatically detecting and measuring tissue changes over time. This substitution of mechanical human observation with automated computational systems dramatically improves measurement precision while reducing analysis time through efficient algorithmic processing.
Solution Approach 2:
The system enables continuous monitoring of lung tissue changes by analyzing serial CT scans over time. Rather than discrete periodic assessments, the quantitative analysis method continuously tracks tissue phenotype evolution, ventilation changes, and disease progression. This continuous action approach maintains high precision in detecting subtle changes while minimizing time loss through automated processing of each time point.
Data Source
AI summary
A voxel-based technique is provided for performing quantitative imaging and analysis of tissue image data. Serial image data is collected for tissue of interest at different states of the issue. The collected image data may be deformably registered, after which the registered image data is analyzed on a voxel-by-voxel basis, thereby retaining spatial information for the analysis. Various thresholds are applied to the registered tissue data to identify a tissue condition or state, such as classifying chronic obstructive pulmonary disease by disease phenotype in lung tissue, for example.