Medical Image Subtraction for Stroke Reperfusion Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current X-ray-based imaging methods, such as digital subtraction angiography (DSA), face challenges in clearly distinguishing between contrasted vessels and tissue due to overlaying contrasted vessels, making it difficult for medical operators to evaluate treatments like stroke reperfusion, as manual adjustments are time-consuming and prone to errors.
Innovation Solution
A method involving the capture of first and second medical image data sets, with the second set capturing contrast agent flow in a time-resolved manner, allowing for the identification of partial image data sets representing physiological subphases, which are used to generate subtraction image data sets that isolate contrasted areas, enabling improved visualization of contrasted tissues and vessels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of parameters (gray-scale windows) is performed to improve tissue consideration, then diagnostic quality is improved, but time consumption and error rate increase
Solution Approach 1:
The system automatically performs parameter adjustment and image processing without requiring manual intervention from the operator. The computer-implemented method autonomously processes the image data sets, identifies physiological subphases, and generates subtraction image data sets, eliminating the need for manual gray-scale window adjustment while maintaining diagnostic quality.
Solution Approach 2:
The manual mechanical process of adjusting gray-scale windows and parameters is replaced by an automated computer-based system. The patent uses computer algorithms to automatically process image data, identify contrast agent flow phases, and generate processed images, substituting manual operations with automated computational processes.
2Adaptability or versatility
If DSA series are used to evaluate treatment success, then treatment evaluation capability is provided, but contrasted vessels overlay contrasted tissue making exclusive consideration of tissue difficult
Solution Approach 1:
The patent segments the image data sets into multiple physiological subphases based on contrast agent flow timing. By dividing the imaging sequence into distinct phases (e.g., arterial phase, capillary phase, venous phase), the system can process and display tissue information separately from vascular information, making tissue visibility improved while maintaining treatment evaluation capability.
Solution Approach 2:
The patent extracts and isolates specific information from the image data by creating subtraction image data sets that highlight particular physiological subphases. This extraction process removes the overlaying effect of vessels on tissue by processing images at specific time points when contrast agent is present in particular structures, thereby separating vascular and tissue signals.
3Measurement precision
If time-resolved capture of contrast agent flow is performed, then phase-specific visualization is enabled, but image processing complexity increases
Solution Approach 1:
The system automatically performs the complex task of identifying physiological subphases and generating phase-specific images without requiring manual intervention. The computer-implemented method autonomously analyzes contrast agent flow patterns, determines phase boundaries, and processes images accordingly, managing the complexity internally while providing simplified output to the user.
Data Source
AI summary
A method for providing a result data set includes: capturing a first medical image data set that maps an object under examination within a first temporal phase; capturing a second medical image data set that maps a flow of contrast agent in the object under examination within a second temporal phase in a time-resolved manner; identifying multiple partial image data sets in the second image data set, wherein the partial image data sets in each case map one of multiple physiological subphases within the second temporal phase; and providing the result data set including multiple subtraction image data sets, wherein each subtraction image data set of the multiple subtraction image data sets is determined based on the first medical image data set and a respective partial image data set of the multiple partial image data sets in the second medical image data set.