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 <https://dipy.org/documentation/1.0.0./examples_built/reconst_fwdti/> 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%* [image: fa_70.png] *MD map with WVF elimination at a threshold of 70%* [image: md_70.png] *RD map with WVF elimination at a threshold of 70%* [image: rd_70.png] *AD map with WVF elimination at a threshold of 70%* [image: 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%* [image: fa_60.png]
*MD map with WVF elimination at a threshold of 60%* [image: md_60.png] *FA map with No WVF elimination threshold* [image: fa_none.png] *MD map with No WVF elimination threshold* [image: 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 <https://drive.google.com/a/temple.edu/file/d/1yvLYeB-cUNqBoce-43ler0-zf_vG8b...> -- *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
