CT-FFR Lesion Classification for Stable Hemodynamic Assessment
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Solution Overview
Problem
Existing CT-FFR techniques face issues with subjectivity in manual measurement positions, variability due to centerline and lumen contour differences, and uncertainty in the 'gray zone' between 0.75 and 0.8, leading to inaccurate and unreliable assessments.
Innovation Solution
A method and system for CT-FFR that classify lesions into types based on branch involvement and calcification, determine focus lengths, and calculate 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 technique is simple to operate, but subjectivity is introduced affecting result uniformity
Solution Approach 1:
The system performs automatic classification of lesions and automatic determination of measurement positions without requiring manual intervention. The computer automatically identifies lesion types (single focal, diffuse, serial lesions) and selects appropriate measurement positions based on pre-established criteria, eliminating operator subjectivity while maintaining ease of use through automated processing
Solution Approach 2:
The system changes the measurement approach by transitioning from fixed manual position selection to dynamic automated selection based on lesion characteristics. Different parameter sets (focus lengths of 5mm, 10mm, 15mm) are automatically applied depending on the classified lesion type, ensuring consistent and objective measurement positions
2Device complexity
If CT-FFR values are measured within a fixed range (2 cm-3 cm distal to stenosis), then the measurement process is standardized, but variability occurs due to differences in centerline points and lumen contour
Solution Approach 1:
The system applies different measurement strategies tailored to specific lesion types. Instead of using a uniform approach for all lesions, it classifies lesions into three types and applies localized measurement parameters: single focal lesions use one focus length, diffuse lesions use another, and serial lesions use yet another. This localized adaptation eliminates variability caused by applying fixed standards to diverse lesion geometries
Solution Approach 2:
The measurement process is segmented into distinct stages: lesion classification, focus length determination, and measurement range calculation. Each segment handles specific aspects of the measurement, with the classification stage dividing lesions into types that then receive type-specific measurement protocols, reducing overall measurement uncertainty
3Quantity of substance
If CT-FFR values in the gray zone (0.75-0.8) are used for diagnosis, then more data is available for assessment, but diagnostic accuracy decreases leading to misjudgment
Solution Approach 1:
The system extracts and identifies CT-FFR values that fall within the problematic gray zone (0.75-0.8) and excludes them from the final diagnostic assessment. By separating these unreliable values from the decision-making process, the system maintains high diagnostic accuracy while still utilizing all available measurement data for comprehensive analysis
Solution Approach 2:
The system incorporates a feedback mechanism that evaluates the distribution of CT-FFR values and identifies when values fall into the gray zone. This feedback triggers automatic adjustment of the diagnostic criteria or exclusion of gray zone values, preventing misjudgment while preserving the utility of having comprehensive measurement data
Data Source
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.