Hi Mahmoud,
Just adding to what Donald said, I want to point out that MP-PCA denoising paper specifically recommends using denoising at the first step of the pipeline (link). A good explanation is given in this paper from the same group:
It is important that the denoising step is the first stage of the pipeline as it relies on noise being uncorrelated both spatially and among successive acquisitions (in the dMRI case, in q-space). Performing this step after processing steps that use interpolation to reconstruct images would result in correlated noise and failure of the basic assumptions underlying the random matrix theory-based approach to PCA denoising.
Unless you are encountering unexpected issues, I would thus recommend sticking to the recommendation of the paper.
W.r.t. specific points in Jesper’s comment:
- As MP-PCA will only suppress white, uncorrelated noise, the output should never be of “artificial nature” (you can check this in the denoising residuals). I see no theoretical reason why this should be incompatible with the Gaussian process regression in
eddy, but is is of course possible thateddymakes certain default assumptions about the expected SNR. - The point about motion effects on denoising is true, any loss of redundancy in the input data by motion and slice dropouts would result in less denoised data. However, first correcting motion introduces noise correlations that will also reduce the capacity to denoise, as explained in the quote.
Cheers!