Conventional fMRI analysis applies spatial Gaussian smoothing to increase SNR, which does not fully utilize multichannel information in fMRI, and often lead to smearing of fMRI images. In this work, we proposed to denoise multichannel fMRI data based on tensor decomposition. Specifically, fMRI data are treated as a 3rd-order tensor, and Canonical Polyadic Decomposition (CPD) is used to approximate fMRI data with sum of limited number of rank-1 terms. Results show its effectiveness in denoising block-design task-related fMRI data, leading to increased temporal SNR and sensitivity of activation detection without sacrificing spatial resolution.
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