Virtual Stroke Imaging via Machine Learning Perfusion Analysis
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Solution Overview
Problem
Current methods for dynamic contrast-enhanced (DCE) imaging in computed tomography (CT) for stroke diagnosis are prone to inaccuracies due to complex mathematical models, require significant processing time, and lack standardization, making it difficult to predict irreversible brain damage and interpret results effectively.
Innovation Solution
A method using a trained machine learning algorithm to process temporally successive tomographic perfusion imaging data sets, enabling the calculation and provision of virtual tomographic stroke follow-up examination images, which automatically segments stroke-damaged tissue and predicts tissue damage without relying on intermediate mathematical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex mathematical models are used to calculate perfusion parameters, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent changes the fundamental parameters used in perfusion analysis from complex mathematical model outputs to direct, simple measurement parameters (maximum enhancement value and time point) that can be read directly from the contrast enhancement curves, eliminating the need for complex modeling while maintaining diagnostic accuracy
Solution Approach 2:
The patent extracts only the essential diagnostic information (maximum enhancement value and time point) from the contrast enhancement curves, removing unnecessary complex calculations and intermediate steps, thereby simplifying the analysis process while retaining the critical data needed for stroke assessment
2Measurement precision
If complex mathematical models are used to calculate perfusion parameters, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent skips the time-consuming intermediate steps of curve adaptation and complex model calculations by directly identifying the maximum enhancement value and its corresponding time point from the contrast enhancement curves, dramatically reducing processing time while maintaining diagnostic accuracy
3Adaptability or versatility
If different mathematical models are used for perfusion analysis, then adaptability is improved, but reliability decreases due to lack of standardization
Solution Approach 1:
The patent establishes a uniform, standardized approach by using the same simple parameters (maximum enhancement value and time point) for all perfusion analyses, ensuring consistent and comparable results across different patients and examinations, thereby improving reliability through standardization
4Measurement precision
If manual curve adaptation is performed, then measurement precision is improved, but ease of operation decreases
Solution Approach 1:
The method enables automatic determination of perfusion parameters by directly reading the maximum enhancement value and time point from the contrast enhancement curves without requiring manual curve adaptation, making the process self-executing and eliminating the need for operator intervention while maintaining accuracy
Data Source
AI summary
A method is disclosed for providing a virtual tomographic stroke follow-up examination image. In an embodiment, the method includes: receiving a sequence of temporally successive tomographic perfusion imaging data sets of a region for examination; calculating the virtual tomographic stroke follow-up examination image of the region for examination by applying a trained machine learning algorithm to the sequence of temporally successive tomographic perfusion imaging data sets received; and providing the virtual tomographic stroke follow-up examination image calculated.