Dwidenoise: correct use

[dchristiaens]

Hi Antonin

@Antonin_Skoch

Could you please additionally comment on if there are situations the dwidenoise use is not advisable? I am especially in doubts in case of FSL BEDPOSTX, where denoising could possibly affect the estimation of “true” uncertainty of model estimates. What do you think?

We recommend to always inspect the residuals after denoising, as explained in the user guide. As long as the residuals don’t contain any structure, I can’t think of any situation in which MP-PCA denoising would bias certain estimates. If effective, denoising may reduce uncertainty on those estimates, but only to the extent that they would be influenced by acquisition noise. I would argue that’s a good thing.

@Antonin_Skoch

I have some older data with 30 directions, 2 repetitions (i.e. 62 volumes). I am preparing the preprocessing for estimation of diffusion tensor parameters (for subsequent TBSS). Would you recommend to run denoising for these data? Is 62 volumes enough? Would you alter window size of such data? What is minimal number of volumes where dwidenoise would reasonably work?

As I said above, if the residuals don’t contain structure then you’re fine. The paper shows results for data with 60 DWI volumes, so that should be sufficient. I expect the default window size of 5x5x5=125 to be a good trade-off too.

@Antonin_Skoch

The N>M rule recommended in tutorial http://mrtrix.readthedocs.io/en/latest/tutorials/denoising.html seems counter-intuitive to me, since it means that for smaller number of volumes I could use smaller window. I would expect opposite, i.e. only with higher number of volumes I can afford to lower the size of window to better grab spatial variance of noise.

Jelle Veraart may be in a better position to comment on this guideline, but I’ll do my best to provide some intuition. MP-PCA is fitting the Marchenko-Pastur distribution to the eigenvalue spectrum of the (patch-based) covariance matrix in each voxel, and uses this fit to select the optimal cut-off in the spectrum. The precision of this cut-off will depend on the “level of detail” in the spectrum histogram, determined by the dimensions of the covariance matrix, equal to the minimum of the no. DWI volumes (M) and the patch size (N). Therefore, you would ideally want both to be as large as possible. In practice, M is fixed and thus sets an upper bound on the rank for your data. If you choose N < M, you further reduce the “level of detail” in the spectrum. On the other hand, if N is too large you can violate the assumed noise homoscedasticity within the patch. Therefore, keep an eye on the residuals when you try different settings.