Lung CT Fissure Analysis for Emphysema Treatment Planning
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Solution Overview
Problem
Existing imaging techniques struggle to accurately determine disease and anatomical features of the lung from CT images, making it difficult to plan effective treatments for pulmonary diseases like COPD, and there is a need for methods to seal collateral flow channels during lung volume reduction procedures.
Innovation Solution
A method for analyzing CT data involves identifying lung structures, calculating fissure completeness scores, and creating 3D reconstructions to generate reports that include emphysema scores and treatment suggestions, using machine learning for personalized treatment planning and navigation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging techniques are used to analyze lung CT data, then the imaging process is simple, but the accuracy of determining disease and anatomical features is insufficient
Solution Approach 1:
The patent segments lung CT data analysis into multiple specialized components: lung structure identification, fissure detection and completeness scoring, emphysema scoring, and volume calculation. Each component focuses on a specific anatomical feature or pathological characteristic, allowing for more accurate measurements while organizing system complexity into manageable modules.
Solution Approach 2:
The patent transforms 2D CT image slices into 3D reconstructions of lung anatomy, enabling volumetric analysis of lung segments, fissures, and emphysematous regions. This dimensional transition allows for more accurate measurement of anatomical features and disease extent that cannot be adequately assessed in two-dimensional images alone.
2Measurement precision
If detailed 3D reconstructions and multiple scoring systems are implemented, then treatment planning accuracy is enhanced, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary automated processing of CT data including lung segmentation, fissure identification, and emphysema scoring before clinical review. By pre-calculating these parameters and generating initial treatment suggestions, the system reduces the time required for detailed treatment planning while maintaining high accuracy through comprehensive analysis.
Solution Approach 2:
The patent implements multiple scoring parameters (fissure completeness scores, emphysema scores, volume measurements) that can be selectively applied based on clinical needs. This allows the system to balance processing time and detail by adjusting which parameters are calculated and reported, providing flexibility in trade-off between comprehensiveness and efficiency.
3Reliability
If collateral flow channels are sealed during lung volume reduction, then treatment effectiveness is improved, but the complexity of the procedure increases
Solution Approach 1:
The patent identifies and segments collateral flow channels (such as channels of Linnegar and Monga) separately from main bronchial structures, allowing for targeted sealing of these specific pathways. This segmentation enables precise intervention on collateral channels without affecting main airways, improving treatment effectiveness while managing procedural complexity through focused attention on specific anatomical features.
Data Source
AI summary
Devices, methods, and systems are provided for analyzing lung imaging data. A server computing device receives imaging data of a lung over a network from a client computing device and analyzes the imaging data to identify lung, airways, and blood vessels, segment the lung into lobes, subtract airways, calculate volumes, calculate emphysema scores, identify fissure locations, calculate fissure completeness scores. A reconstruction of the fissures indicating locations where the fissures are incomplete and a report comprising fissure scores, volumes, and emphysema scores are created.