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Abstract #1100

Optimal Singular-Value Shrinkage for fMRI Denoising

Mo Shahdloo1 and Mark Chiew1
1Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB, University of Oxford, Oxford, United Kingdom


Singular-value truncation techniques have shown promise for reducing thermal noise in fMRI, where singular-values below a certain threshold are assumed to be noise and are discarded. However, this approach could lead to suboptimal signal recovery, since the remaining singular-values could still have variance contributed by noise. Here instead we propose to use a theoretically MSE-optimal function to shrink the remaining singular-values. The proposed method is evaluated using simulations and high-resolution in-vivo human brain data, and is shown to improve signal-to-noise ratio and functional statistics while leaving the spatial precision intact.

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