Denoising of fMRI Volumes Using Local Low Rank Methods

Functional Magnetic Resonance Imaging (fMRI) data provides deep insight on brain activity, but high-resolution (e.g. 1 mm isotropic) fMRI suffers from low signal-to-noise ratio (SNR). Recently several denoising methods have been proposed to boost the SNR of high-resolution fMRI images. In this work we carry out a prospective benchmark of local low-rank denoising methods by quantifying their performances on a retinotopy experimental paradigm that was submitted to six healthy volunteers during a 7 Tesla, 1 mm isotropic fMRI acquisition protocol. Out of the five tested approaches (NORDIC, MP-PCA, Hybrid-PCA, Optimal Threshold (OT) and Hybrid-OT), the OT approach outperforms its competitors in terms of statistical sensitivity and specificity both at the subject- and group-level. Additionally, by testing these denoising methods in different configurations of preprocessing pipelines, we demonstrate first that on average it is beneficial to denoise fMRI images prior to performing realignment (i.e. motion correction), and second that the OT approach performs better when applied to complex-valued fMRI images instead of magnitude-only ones. We also provide an open source implementation to promote a broader use of denoising methods in fMRI and enable reliable statistical data analysis at high spatial resolution.