Temporal Derivative Analysis for X-Ray Blood Flow Evaluation
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Solution Overview
Problem
Existing evaluation methods for temporal sequences of x-ray images do not effectively highlight blood flow information into vessels, limiting their precision in determining perfusion and vascular dynamics.
Innovation Solution
A computer-based method that analyzes the temporal derivative of data values in x-ray images to identify local maxima and minima, assigning types to evaluation regions based on these derivatives, allowing for more precise characterization of blood flow patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the temporal course of data values is analyzed directly, then the evaluation can be performed with simpler processing, but the blood flow information is not highlighted effectively
Solution Approach 1:
The patent transforms the evaluation from analyzing raw data values to analyzing the temporal derivative of data values. This parameter transformation highlights blood flow information by emphasizing rate of change, making perfusion dynamics more visible while maintaining systematic processing capability.
2Loss of information
If the temporal derivative is calculated and analyzed, then blood flow dynamics become more visible, but the processing complexity increases
Solution Approach 1:
The patent applies temporal differentiation to transform the time-series data, converting absolute data values into rates of change. This parameter transformation makes blood flow dynamics more prominent and easier to interpret, addressing the information visibility issue.
Solution Approach 2:
The patent replaces direct visual inspection of raw data with automated derivative calculation and type assignment algorithms. This substitution of manual/mechanical analysis with computational processing handles the increased complexity systematically.
3Measurement precision
If type assignment is performed for all pixels, then comprehensive evaluation is achieved, but the processing time increases
Solution Approach 1:
The patent segments the evaluation space into standard evaluation regions (such as myocardial segments) and processes each region independently with type assignment. This segmentation allows comprehensive evaluation to be performed in a structured, efficient manner across the entire image.
Solution Approach 2:
The patent applies a universal type assignment methodology that can be used across all evaluation regions and different imaging scenarios. This standardized approach enables comprehensive evaluation while reducing processing time through consistent application of the same algorithmic framework.
Data Source
AI summary
A computer receives a temporal sequence of x-ray images of an examination region of an examination object. The examination region includes a blood vessel system and tissue supplied with blood. A detection time is assigned in each instance to the x-ray images. The x-ray images correspond locally with one another in terms of pixels and each display a distribution of a contrast agent in the examination region at the respective detection time. The computer determines the temporal course of the temporal derivation of the data values and/or of the average value of the data values of the pixels located in the evaluation region for at least one evaluation region which is standard for all x-ray images. It assigns a type to the evaluation region as a function hereof.