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Hi Tugan,

Thank you for your question!

This effect that you are seeing could be happening due to one of 2 reasons:

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.
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

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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
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