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Abstract #2164

Patch based low rank and sparse decomposition for arterial spin labeling perfusion MRI signal denoising

Hancan Zhu1, Jian Zhang2, and Ze Wang3

1Department of Mathematics, Shaoxing University, Shaoxing, China, 2Institutes of Psychological Science, Hangzhou Normal University, Hangzhou, China, 3Department of Radiology, Temple University, Philadelphia, PA, United States

Arterial spin labeling (ASL) perfusion fMRI has much less neurovascular effects than BOLD fMRI, but its application in time-series analysis is still depreciated due to the low signal-to-noise-ratio (SNR). In this study, we propose a patch based low rank and sparse decomposition method to denoise ASL MRI. Our results showed that the proposed method can markedly increase the sensitivity of ASL MRI-based task activation detection.

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