Keywords: fMRI Analysis, fMRI Analysis, denoising, ICA, multi-echo fMRI, U-Network
Motivation: fMRI data is noisy and the standard denoising technique used in multi-echo (ME) fMRI is ICA-based (ME-ICA). Consequently, its performance depends on how well ICA is able to separate noise from non-noise components.
Goal(s): To evaluate the potential of using an U-convolutional network to denoise ME-fMRI data.
Approach: The efficacy of our denoising U-network (DUNE) was compared to ME-ICA in task-based ME-fMRI by looking at the residual noise and the activation maps.
Results: DUNE was found to be effective in reducing the noise while preserving the BOLD response of interest while ME-ICA failed to denoise the data.
Impact: As a first step in the development of a new denoising technique that is not ICA-dependent, this work showed the potential of using an U-convolutional network to denoise multi-echo fMRI data as an alternative to ICA-based methods.
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