MP-PCA denoising has become the method of choice for denoising in MRI since
it provides an objective threshold to separate the desired signal from unwanted
thermal noise components. In rodents, thermal noise in the coils is an
important source of noise that can reduce the accuracy of activation mapping in
fMRI. Further confounding this problem, vendor data often contains zero-filling
and other effects that may violate MP-PCA assumptions. Here, we develop an
approach to denoise vendor data and assess activation "spreading" caused by
MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3
mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms
temporal resolution, respectively), during visual stimulation. MP-PCA denoising
produced SNR gains of 64% and 39% and Fourier spectral amplitude (FSA)
increases in BOLD maps of 9% and 7% for multislice and ultrafast data,
respectively, when using a small [2 2] denoising window. Larger windows
provided higher SNR and FSA gains with increased spatial extent of activation
that may or may not represent real activation. Simulations showed that MP-PCA
denoising causes activation "spreading" with an increase in false positive rate
and smoother functional maps due to local "bleeding" of principal components,
and that the optimal denoising window for improved specificity of functional
mapping, based on Dice score calculations, depends on the data's tSNR and
functional CNR. This "spreading" effect applies also to another recently
proposed low-rank denoising method (NORDIC). Our results bode well for
dramatically enhancing spatial and/or temporal resolution in future fMRI work,
while taking into account the sensitivity/specificity trade-offs of low-rank
denoising methods