Stroke Perfusion Mapping Using Deconvolution and Cluster Analysis
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Solution Overview
Problem
Current methods for identifying ischemic lesions in acute stroke, particularly in distinguishing between penumbra and infarct regions, face challenges due to overestimation and underestimation of ischemic lesion extent caused by arterial delay and dispersion effects, and are prone to false positives from image noise, especially in dynamic CT perfusion data with low radiation dosage.
Innovation Solution
A method involving motion correction, derivation of perfusion maps using model-free deconvolution techniques like singular value decomposition (SVD), and iterative deconvolution to account for arterial delay and dispersion effects, combined with cluster analysis to accurately identify ischemic lesions and differentiate between penumbra and infarct regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple threshold method is used to identify ischemic lesion, then the identification process is simple and fast, but the measurement precision deteriorates due to overestimation from arterial delay and dispersion effects
Solution Approach 1:
The patent applies preliminary action by performing deconvolution processing before threshold-based identification. The arterial input function is deconvolved from tissue perfusion curves to obtain impulse residue functions, and CBF maps are generated before applying thresholds. This preliminary deconvolution step corrects for arterial delay and dispersion effects, ensuring that subsequent threshold-based identification achieves both speed and accuracy.
2Ease of operation
If global AIF from large artery is used, then the measurement process is simplified, but the measurement precision deteriorates due to delay and dispersion effects
Solution Approach 1:
The patent uses deconvolution as an intermediary mathematical operation to eliminate the harmful effects of arterial delay and dispersion. By deconvolving the global AIF from the tissue perfusion curves, the method recovers the true tissue impulse residue function, effectively removing the distorting influence of the arterial transport process while maintaining the simplicity of using global AIF from large arteries.
3Object-affected harmful factors
If low radiation dosage protocol is used, then the patient safety is improved, but the reliability deteriorates due to increased image noise causing false positives
Solution Approach 1:
The patent applies feedback through iterative deconvolution processes that use the measured global AIF and tissue perfusion curves to continuously refine the estimation of CBF and identify ischemic lesions. The method iteratively adjusts the identification based on the deconvolved impulse residue functions, allowing the system to distinguish true ischemic signals from noise by comparing against the actual measured perfusion dynamics rather than relying on noisy single-timepoint data.
4Measurement precision
If deconvolution technique is applied to account for arterial delay and dispersion, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or physiological measurement systems with mathematical deconvolution processing. Instead of attempting to directly measure arterial delay and dispersion parameters through complex imaging or catheter-based systems, the method substitutes these physical measurement challenges with computational deconvolution of the measured perfusion curves, achieving high precision through mathematical rather than mechanical means.
Data Source
AI summary
The current invention provides a method of identifying a ischemic lesion. The method includes loading perfusion imaging data into an electronic memory element and deriving perfusion maps from the perfusion imaging data, where the perfusion maps include a cerebral blood volume (CBV) map and an arterial delay time (DT) map, which utilize arterial delay and dispersion effects. Ischemic pixels are determined from the perfusion imaging data, where the DT is greater than a predetermined first threshold value and the CBV is below a second threshold value and the infarct portion of the ischemic lesion is determined, where DT is greater than a predetermined third threshold value and/or the CBV is below a forth threshold value. A cluster analysis is applied to all of the determined ischemic lesion and infarct pixels and the penumbra is then determined, where mismatch regions between the ischemic lesion and the infarct core define the penumbra.