Hi Tugan, Thank you for your question! This effect that you are seeing could be happening due to one of 2 reasons: 1. The b0_threshold parameter used in DIPY is set to 50. If the value of your b0 volumes is greater than 50 in your bval, it would be assumed as a DWI volume, which may cause this effect. 2. You need to set the parameters shift_intensity=True and clip_negative_vals=False within Patch2Self. If setting both of these above points don't work for you, you can also skip denoising b0 volumes by using the parameter: b0_denoising=False If possible, I can also take a look at your dataset and help you with it! Regards, Shreyas ________________________________ From: lmuftuler--- via DIPY <dipy@python.org> Sent: Wednesday, March 31, 2021 5:10 PM To: dipy@python.org <dipy@python.org> Subject: [External] [DIPY] patch2self denoising attenuated b=0 images This message was sent from a non-IU address. Please exercise caution when clicking links or opening attachments from external sources. ------- Hi, I was trying out the new patch2self denoising on a 4-shell (b=1k s/mm^2 up to 4k s/mm^2) diffusion data (total of 94 directions plus 4 b=0 images). First, I noticed that it attenuated the b=0 images (all four of them) with respect to the rest of the image series. This in turn reduced map-mri ZD maps by about 10% to 20%, which was expected b/c (E(q)) essentially got "smaller". Did anybody have any experience with this? Any suggestion why it happened and how to fix it? best, -Tugan _______________________________________________ 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