Hi Dani, Sorry for the slowness on our end. In my opinion, Marchenko-Pastur PCA should be used as the default. 2 things to ensure, 1. Keep patch_radius parameter = 2 by default. If you get an ill-conditioned error, increase the patch radius. 2. Marchenko-Pastur PCA is considered to be a non-aggressive denoiser, meaning that it leaves in some noise at the cost of not removing any signal. You can follow this tutorial - https://dipy.org/documentation/1.1.1./examples_built/denoise_mppca/#example-... where we show the effect of denoising on DKI parameter maps. You should be able to see a similar reduction in degeneracies in FW-DTI (if any) due to noise suppression. If the noise in your data is too high, I would go for the empirical Local PCA/ NLMeans. Where you will need to play around with the sigma parameter a bit. Let us know how this goes or if you need any more help on our end! Regards, Shreyas ________________________________________ From: Dani Bergé <dbergeba@gmail.com> Sent: Wednesday, May 6, 2020 7:29 AM To: dipy@python.org Subject: [External] [Dipy] Which denoise process? pros and cons. This message was sent from a non-IU address. Please exercise caution when clicking links or opening attachments from external sources. ------- Dear Dipy developers and users, I am planning to preprocess DWI data to study free-water in both white matter and grey matter. I have notice that there are at least 3 differnt denoising options (NLMEANS, local PCA and Marcenko-Pasteur PCA) and I would like to kindly ask (whoever wants to answer) , which are the pros and cons of each one, or which one is most commonly used by default. Thanks in advance Dani Bergé Hospital del Mar, Barcelona, Spain _______________________________________________ Dipy mailing list -- dipy@python.org To unsubscribe send an email to dipy-leave@python.org https://mail.python.org/mailman3/lists/dipy.python.org/ Member address: shfadn@iu.edu