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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