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Abstract #2873

Improving ASL Perfusion MRI-based Functional Connectivity Analysis with Robust Principal Component Analysis

Ze Wang1

1Hangzhou Normal University, Hangzhou, China, People's Republic of

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). Robust principal component analysis (RPCA) decomposing the original data into a smoothly varying low-rank component and a residual component with sparse signal. In this study, we used RPCA to denoise ASL MRI. Our results showed that RPCA can markedly increase the sensitivity of ASL MRI-based functional connectivity analysis.

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