The ONLM denoising algorithm in the following package: (MailScanner has detected a possible fraud attempt from "na01.safelinks.protection.outlook.com" claiming to be https://sites.google.com/site/pierrickcoupe/softwares/denoising-for-medical-imaging)

Best,

-Lisa

From: freesurfer-bounces@nmr.mgh.harvard.edu [mailto:freesurfer-bounces@nmr.mgh.harvard.edu] On Behalf Of Glasser, Matthew
Sent: Thursday, October 11, 2018 12:39 PM
To: Freesurfer support list
Subject: Re: [Freesurfer] denoising and recon-all

External Email - Use Caution

What are you using for denoising?

Matt.

From: <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Lisa Crystal Krishnamurthy <lkrishnamurthy@gsu.edu>
Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date: Thursday, October 11, 2018 at 11:05 AM
To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
Subject: [Freesurfer] denoising and recon-all

External Email - Use Caution

Hi,

I have found that denoising the MPRAGE prior to FS recon-all seems to improve the segmentation (see attached pdf). However, it is not clear if the denoising algorithm may cause some signal intensity changes (especially in voxels with partial voluming at the edge of the brain) that violate assumptions of recon-all. Could you help me understand what the assumptions of your algorithm are, and what I need to do to make sure my images conform to those assumptions?

Your help is greatly appreciated.

Best,

-Lisa

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