CT-FFR Lesion Typing for Consistent Hemodynamic Classification
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Solution Overview
Problem
Existing CT-FFR methods suffer from subjectivity in manual measurement positions, uncertainty due to centerline and lumen contour variations, and diagnostic ambiguity in the 'gray zone' between 0.75 and 0.8, leading to inaccurate assessments.
Innovation Solution
A method for classifying lesions into types based on branch involvement and calcification, determining focus lengths, and calculating CT-FFR values within specific measurement ranges using statistical analysis to enhance accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual measurement positions are selected for CT-FFR, then the measurement process is simple, but subjectivity is introduced affecting result uniformity
Solution Approach 1:
The system automatically identifies lesion types and determines measurement positions based on CCTA image analysis, eliminating manual intervention. The computer automatically selects centerline points and calculates CT-FFR values at standardized positions relative to each lesion type, ensuring consistency while maintaining operational simplicity.
Solution Approach 2:
The patent establishes different measurement position parameters for different lesion types (single focal, diffuse, serial lesions). For example, single focal lesions use positions 2cm and 3cm distal to the lesion, while serial lesions use positions relative to each individual lesion within the serial group. This parameter-based approach standardizes measurements while adapting to lesion characteristics.
2Reliability
If CT-FFR values are measured within 2cm-3cm distal to stenosis, then the measurement follows standard protocol, but centerline points and lumen contour differences cause value variations
Solution Approach 1:
The patent segments the coronary artery into different lesion types (single focal lesion, diffuse lesion, serial lesion) and applies specific measurement strategies to each segment. For serial lesions, the system further segments them into individual lesions within the serial group and determines measurement positions for each, ensuring that centerline and lumen variations are handled appropriately for each lesion segment.
Solution Approach 2:
Different measurement approaches are applied to different lesion types based on their local characteristics. Single focal lesions use fixed distance measurements (2cm, 3cm distal), while serial lesions use relative positioning within the serial group. This local adaptation ensures measurement consistency despite variations in centerline and lumen contour across different lesion types.
3Quantity of substance
If CT-FFR values in the gray zone (0.75-0.8) are used, then more data points are available, but diagnostic accuracy decreases leading to misjudgment
Solution Approach 1:
The system calculates CT-FFR values at multiple positions (2cm and 3cm distal to lesions) and uses statistical analysis to determine the most representative value. By comparing values from different positions and applying classification thresholds, the system provides feedback to identify the most diagnostically accurate measurement, avoiding the gray zone ambiguity.
Solution Approach 2:
Instead of using a single CT-FFR value, the patent calculates values at multiple positions (excessive action) and uses statistical methods to select the most representative value. This partial use of multiple data points rather than all available data helps avoid gray zone misjudgments while maintaining diagnostic accuracy.
Data Source
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AI summary
A method, system and device for performing measurement and classification for CT-FFR, and a storage medium, for dividing lesions into different types according to a Coronary Computed Tomography Angiography (CCTA) image, determining focus lengths of lesion positions according to different lesion types, then determining Computed Tomography Fractional Flow Reserve measurement ranges according to the focus lengths of different lesion types, establishing statistical matrices to subject all Computed Tomography Fractional Flow Reserve (CT-FFR) values within the measurement ranges to calculation, and finally subjecting the calculation results to hemodynamical classification. By performing lesion specific classification of lesions in a hemodynamical sense, the accuracy and stability of CT-FFR assessment is enhanced, interference is reduced, and a more reliable solution is provided for clinical physicians and patients.