Hello,

I am currently trying to set up my first diffusion kurtosis imaging pipeline using DKE and DKE_FT (thank you to the creators for these awesome tools!) and I have two questions.

Firstly, looking at the output from DKE, there is a bit of noise left in my data. I have included denoising and Gibbs ringing correction in my preprocessing as well as FSLs eddy and top up. I am thinking about using the robust tensor fit option in DKE (i.e., RESTORE) to hopefully address remaining outliers, but am wondering what I should do with the other tensor fit options if I do this. i.e., should I continue using the default weighted constrained linear least squares method if I also include the robust option? Do you think that this is a good option for cleaning up the data, or should I include a different method of outlier detection somewhere else in my analysis? 

Secondly, After putting my preprocessed images through DKE, I am hoping to do 2 things: run the FA/kurtosis images through TBSS, and run tractography. For TBSS, the images are registered to standard space within FSL's TBSS functions, which is great. But my question is whether I should also be registering the images to standard space before I run DKE_FT, and what tool you would recommend for doing this. 

I apologise if these is a silly questions and appreciate any advice you may have. 

Thank you for your help! 

Kindest Regards,
Meg