Comparing the efficacy of data-driven denoising methods for a multi-echo fMRI acquisition at 7T

• Published in: NeuroImage (Orlando, Fla.) Vol. 280; p. 120361
• Main Authors: Beckers, Abraham B., Drenthen, Gerhard S., Jansen, Jacobus F.A., Backes, Walter H., Poser, Benedikt A., Keszthelyi, Daniel
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15-10-2023; Elsevier Limited; Elsevier
• Subjects: 7 Tesla; Aroma; Brain mapping; Brain stem; Brainstem; Cerebrospinal fluid; Datasets; Denoising; Entropy; Functional magnetic resonance imaging; Magnetic resonance imaging; Multi-echo; Neural networks; Neuroimaging; Physiology; Principal components analysis; Regression analysis; Respiration; Scanners; Multi-echo; Brainstem; 7 Tesla; Denoising
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.120361

•ME-ICA and ICA-AROMA provide effective denoising for multi-echo 7T fMRI data.•High tSNR can be achieved in the brainstem with a multi-echo acquisition at 7T.•After ME-ICA, the data is best post-processed to correct for spatially diffuse noise.•ICA-AROMA-aggr might be too aggressive in denoising the multi-echo 7T fMRI data.
In functional magnetic resonance imaging (fMRI) of the brain the measured signal is corrupted by several (e.g. physiological, motion, and thermal) noise sources and depends on the image acquisition. Imaging at ultrahigh field strength is becoming increasingly popular as it offers increased spatial accuracy. The latter is of particular benefit in brainstem neuroimaging given the small cross-sectional area of most nuclei. However, physiological noise scales with field strength in fMRI acquisitions. Although this problem is in part solved by decreasing voxel size, it is clear that adequate physiological denoising is of utmost importance in brainstem-focused fMRI experiments. Multi-echo sequences have been reported to facilitate highly effective denoising through TE-dependence of Blood Oxygen Level Dependent (BOLD) signals, in a denoising method referred to as multi-echo independent component analysis (ME-ICA). It has not been explored previously how ME-ICA compares to other data-driven denoising approaches at ultrahigh field strength. In the current study, we compared the efficacy of several denoising methods, including anatomical component based correction (aCompCor), Automatic Removal of Motion Artifacts (ICA-AROMA) aggressive and non-aggressive options, ME-ICA, and a combination of ME-ICA and aCompCor. We assessed several data quality metrics, including temporal signal-to-noise ratio (tSNR), delta variation signal (DVARS), spectral density of the global signal, functional connectivity and Shannon spectral entropy. Moreover, we looked at the ability of each method to uncouple the global signal and respiration. In line with previous reports at lower field strengths, we demonstrate that after applying ME-ICA, the data is best post-processed in order to remove spatially diffuse noise with a method such as aCompCor. Our findings indicate that ME-ICA combined with aCompCor and the aggressive option of ICA-AROMA are highly effective denoising approaches for multi-echo data acquired at 7T. ME-ICA combined with aCompCor potentially preserves more signal-of-interest as compared to the aggressive option of ICA-AROMA.