Aortic Valve Calcification Assessment Using Adaptive CT Thresholding
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Solution Overview
Problem
Current methods for assessing aortic valve calcification using contrast-enhanced computed tomography (CT) face challenges in accurately quantifying calcification due to variability in luminal attenuation and the need for manual annotation, leading to underestimation or overestimation of calcific deposits, especially in severe cases.
Innovation Solution
A novel method that uses an iterative region growing approach based on intensity characteristics of the aortic root segmentation, adjusting the Hounsfield units threshold to minimize false positives, and generating anatomically based regional maps for precise quantification of calcification, including geometric parameters like volume and intensity, without relying on luminal attenuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard Agatston technique is used with contrast-enhanced CT images, then calcification assessment can be performed, but measurement precision deteriorates due to variability in luminal attenuation and false positives
Solution Approach 1:
The patent dynamically adjusts the Hounsfield unit threshold based on the actual luminal attenuation measured in each patient's scan, rather than using a fixed threshold. This adapts the detection parameters to the specific contrast enhancement level, eliminating false positives while maintaining sensitivity to true calcification.
Solution Approach 2:
The system uses the measured luminal attenuation as feedback to automatically adjust the calcification detection threshold. By measuring the actual contrast enhancement in the aortic lumen and using this information to set the threshold, the system creates a closed-loop approach that ensures accurate differentiation between contrast-filled lumen and calcific deposits.
2Loss of information
If manual annotation is used for calcification assessment, then detailed regional information can be obtained, but device complexity and time consumption increase
Solution Approach 1:
The patent automatically segments the aortic valve into distinct regions (coronary cusps, non-coronary cusp, sinuses of Valsalva) using image processing algorithms. This automated segmentation provides detailed regional calcification maps without requiring manual annotation, preserving all spatial information while eliminating the complexity and time requirements of manual methods.
Solution Approach 2:
The system performs self-annotation by automatically identifying and characterizing calcification regions within each anatomical segment of the aortic valve. The algorithm independently processes the images to generate regional calcification assessments without human intervention, maintaining full informational detail while simplifying the overall process.
3Productivity
If fixed Hounsfield unit threshold is used for calcification detection, then processing speed is maintained, but measurement precision deteriorates due to contrast enhancement variability
Solution Approach 1:
The patent transforms the static fixed threshold approach into a dynamic adaptive threshold system. The threshold automatically adjusts based on the measured luminal attenuation in each scan, allowing the system to maintain high processing speed while achieving accurate calcification detection across varying contrast enhancement levels.
Data Source
AI summary
Described is a method for assessing aortic valve calcification using contrast-enhanced CT. The method comprises: receiving CT images of the aortic valve; pre-processing received images to have a field of view focused on an aortic root corresponding to the aortic valve; implementing a fast-marching method for the pre-processed images to segment the aortic valve from surrounding tissue to generate an aortic root model; determining multiple principal axes and multiple landmark points based on the pre-processed images and the aortic root model to define a local coordinate system relative to leaflets of the aortic valve; generating a calcification model based on the pre-processed images and aortic root model by iteratively changing an initial estimate of calcific HU threshold until a minimum false positive rate FPR criterion is reached; and generating an indicator quantifying calcification of the aortic valve based on the calcification model, the principal axes and the landmark points.