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?
On Wed, Jul 1, 2020 at 12:22 PM Linda Jasmine Hoffman <tuf72977(a)temple.edu>
> 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
> - 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
> 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
> 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,
> *Lab Manager*
> *Cognitive Neuroscience Lab*
> Temple University
> 1701 N. 13th St.
> Philadelphia, PA 19122
> *Pronouns: * She/Her
> *Phone*: (215) 204-1708
> *Email*: tuf72977(a)temple.edu
I need your help and your opinion.
I would like to run a free-water correction of DTI by using dipy.
Only to be sure: is it possible to use a single-shell data, with a b=1000?
Additionally, I have other data with 26 directions (I do not like to use
<30 directions, but I have this kind of data).
The 'issue' is that the b-values are:
*0 50 250 450 650 850 100 300 500 700 900 100 300 500 700 900 150 350 550
750 950 200 400 600 800 1000*
Therefore, all b-values are different.
I have never worked with this kind of data before. In your opinion, is it
possible to have a correct DTI fit and analysis (and also free-water
correction) with this data?
Maurizio Bergamino, Ph.D
Research Imaging Specialist
Keller Center for Imaging Innovation
Barrow Neurological Institute / Saint Joseph's Hospital and Medical Center
Phoenix, Arizona, USA