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Creating a Sustainable Society

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Sustainable Energy Engineering

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National Science Foundation Day

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Reformulation-Based Approaches to Query Optimization

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WEST: Cloning Data Cache Behavior using Stochastic Traces

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Computer Vision and Image Analysis in the Study of Art

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Skewed functional processes and their applications

An ICA Approach to Remove Recirculation and Tracer Arrival Time Effects on Flow Estimation...

In Dynamic susceptibility contrast perfusion weighted imaging (PWI), recirculation is normally removed by gamma variate fitting from concentration curves before estimate hemodynamic parameters. However, it cannot consistently discern recirculation for cerebral ischemia, where a broadened curve leading to the first passage overlapping with recirculation. We propose using independent component analysis (ICA) to remove recirculation from concentration curves. We demonstrate that ICA can remove recirculation in normal and ischemic brain tissues while preserving the first passage. The ability to accurately remove recirculation should improve CBV and CBF accuracy particularly when overlapping between first passage and recirculation is suspected such as the ischemic lesion.

Cerebral blood flow (CBF) can be estimated by deconvolving the recirculation free concentration curves with an arterial input function (AIF). Normally, a global AIF measured in large vessels is used to estimate CBF for all brain voxels, which inevitably induces delays and dispersion between the global AIF and concentration curves. Hence, using a properly defined local AIF to estimate CBF for each region can yield more accurate results. We also propose here to use ICA to identify the time delay between the global AIF and the local AIF for each 5×5 ROI. The global AIF is then shifted to correct the time offset with respect to the tissue curves in that ROI, while preserving the shape of the global AIF. Our results show enhanced CBF and reduced mean transit time (MTT) values in the ischemic brain tissues comparing to the global AIF approach.