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Abstract #2914

Denoise functional magnetic resonance imaging with variance-stabilizing transformation and optimal singular value shrinkage (VST-SVS)

Wei Zhu1, Xiaodong Ma1, Xiao-Hong Zhu1, Kamil U─čurbil1, Wei Chen1, and Xiaoping Wu1
1University of Minnesota, Minneapolis, MN, United States

High-resolution fMRI is largely hindered by random thermal noise. In this study, we propose a denoising method to reduce such noise in magnitude fMRI data. The proposed method synergistically combines: 1) variance-stabilizing transformation to convert Rician data to Gaussian-like data, 2) principle-component-analysis-based denoising algorithm with optimal singular value shrinkage to remove noise, and 3) patch-based implementation with tunable Gaussian weighting to tradeoff between functional sensitivity and specificity. Our results using synthetic and in-vivo cat task fMRI data show that the denoising method can effectively remove Rician noise, promoting functional fidelity and sensitivity in comparison to existing denoising methods.

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