Hi everyone,

I just wanted to touch base with you to see if you've had the opportunity to give my previous email some consideration.  Please let me know what my next steps should be re: denoising my DWI data to eliminate excessive ventricular artifacts post-fwc.

Thank you!
Linda

On Thu, Jul 2, 2020 at 12:41 PM Linda Jasmine Hoffman <tuf72977@temple.edu> wrote:
Hi Ariel,

Our preprocessing pipeline includes the following steps for noise reduction in FSL:
  • topup - correct for the susceptibility induced field and movement
  • eddy - correct for eddy current distortions and movement
We don't have a step in our pipeline to correct for Gibbs artifacts.  Do you think this particular type of artifact is what's underpinning this issue with the FWC scalar maps?  If so, I found a command in MRtrix3 (mrdegibbs) that eliminates ringing, but it will necessitate that I go back and redo a large amount of preprocessing.  Do you know of an alternative route to mitigate this problem that may obviate my need to reprocess my data?

Thank you so much for your help!
Kind regards,
Linda

On Thu, Jul 2, 2020 at 12:45 AM Ariel Rokem <arokem@gmail.com> wrote:
Hi Linda, 

With your permission, I am adding the DIPY mailing list, so others can weigh in and/or benefit from the discussion. 

My hunch is that the noise you are seeing in the ventricles is due to artifacts/noise. Do you do any removal of Gibbs ringing artifacts or any denoising of the data before analyzing it with fwdti? 

Cheers, 

Ariel


On Wed, Jul 1, 2020 at 12:22 PM Linda Jasmine Hoffman <tuf72977@temple.edu> wrote:
Good afternoon DIPY experts,

My name is Linda Hoffman, and I'm the lab manager for Dr. Ingrid Olson's Cognitive Neuroscience Lab at Temple University.  I have been working on implementing a DIPY-based free-water elimination (FWE) pipeline that my labmate, Katie Jobson, adapted from your website in order to extract free-water corrected (FWC) scalar maps from a HYDI dataset that I'm analyzing.  For your reference, I am ultimately planning to calculate FWC DTI metrics for the fornix and genu of the corpus callosum after performing probabilistic tractography.  I have preprocessed my data using FSL version 6.0 and MRtrix3 on a linux machine.

While I have successfully extracted FWC FA, MD, RD, and AD maps from my data using this pipeline, there still seems to be a disproportionate amount of noise in the ventricles, especially when comparing my output to your examples on the website linked above.  This is the case even after eliminating voxels with a water volume fraction (WVF) exceeding 70%.  In light of this, I was wondering if you may be able to address the following questions:
  • Is the amount of ventricular noise post-FWE in my scalar maps within a normal range?  Will this preclude me from extracting valid FWC DTI metrics from the fornix and the genu?  Here are some screenshots from a representative subject's scalar maps: 
FA map with WVF elimination at a threshold of 70%
fa_70.png
MD map with WVF elimination at a threshold of 70%
md_70.png
RD map with WVF elimination at a threshold of 70%
rd_70.png
AD map with WVF elimination at a threshold of 70%
ad_70.png
  • If this noise is not within an acceptable range, how might I be able optimize our DIPY script so that I can perform a better FWE?  I tried comparing the results from using a stricter WVF threshold of 60% as well as using no WVF thresholding to the above results.  Using a stricter threshold did not completely eliminate the noise problem, but it did help a little bit.  However, I'm not sure if there is a precedent for this level of thresholding in the literature, or if it is actually appropriate.  Screenshots from a representative subject are listed below:
FA map with WVF elimination at a threshold of 60%
fa_60.png
MD map with WVF elimination at a threshold of 60%
md_60.png
FA map with No WVF elimination threshold
fa_none.png
MD map with No WVF elimination threshold
md_none.png

I have attached a zip file with the following information for your reference:
  1. Input data from a representative subject.  This includes DWI volumes collected at b values between 0 to 2000.  This is contained in the subject_data subfolder.
  2. Scalar maps collected with a WVF thresholding rate of 70% (F>.7), 60% (F>.6), and with no thresholding (no_F_threshold).
  3. Three versions of the DIPY script I've been using - each one accounts for a different rate of WVF thresholding.  These scripts are contained in the dipy_fwe_script_versions subfolder.
I sincerely appreciate all of your time and consideration, and look forward to hearing from you soon!

Kind regards,
Linda

--
Lab Manager
Cognitive Neuroscience Lab
Temple University
1701 N. 13th St. 
Philadelphia, PA 19122

Pronouns:  She/Her
Phone:  (215) 204-1708


--
Lab Manager
Cognitive Neuroscience Lab
Temple University
1701 N. 13th St. 
Philadelphia, PA 19122

Pronouns:  She/Her
Phone:  (215) 204-1708


--
Lab Manager
Cognitive Neuroscience Lab
Temple University
1701 N. 13th St. 
Philadelphia, PA 19122

Pronouns:  She/Her
Phone:  (215) 204-1708
Email:  tuf72977@temple.edu