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

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

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