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Abstract #2347

Introducing ME-DUNE, a denoising U-network applied in a task based multi-echo fMRI study.

Peter Van Schuerbeek1, Manon Roose2, Hubert Raeymaekers3, and Maarten Naeyaert3
1Radiology, UZ Brussel (VUB), Brussel, Belgium, 2radiology, UZ Brussel (VUB), Brussel, Belgium, 3UZ Brussel (VUB), Brussel, Belgium

Synopsis

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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Keywords