7 research outputs found

    Example of the automated TSP maps in an ischemic stroke patient with a right-sided perfusion deficit.

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    <p>The images were generated using an iterative method that first calculated an average time-series of healthy tissue (top left) to generate a Pearson’s correlation map of all voxels in the brain based on correlation with the average time-series of healthy tissue (top right). Signal intensity time-series for voxels in healthy and under perfused tissue are in the bottom panel. Voxels in healthy tissue demonstrate a signal intensity time-series that is like the average signal intensity time-series for all healthy tissue, while a voxel in the perfusion deficit will have a signal intensity time-series that is delayed, dispersed and/or decreased.</p

    Example of TSP maps with varying Pearson’s R correlation thresholds in a patient with a right-sided perfusion deficit.

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    <p>The red encircled map shows the threshold used for the automated lesion detection. No visual differences were found between TSP maps constructed with these different thresholds (B) Mean signal values for lesion and healthy tissue (based on the unbiased perfusion lesion) showed relatively stable TSP values within TSP maps for healthy and lesion tissue demonstrating robustness. (C) The mean difference in signal between healthy and perfusion tissue increased slightly (by ~0.03 in the 0.9 map compared to the 0.6 map) in TSP values.</p
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