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Robust tensor fitting

Submitted by vanania on Tue, 02/04/2020 - 07:21
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Dear Tortoise community,

I am a new Tortoise user and downloaded version 3.2.0 for Linux.

In this moment, I would need to estimate the diffusion tensor using the robust RESTORE approach. I have a few questions about how to correctly run the command EstimateTensorNLLSRESTORE:

  • Does the RESTORE implementation available in TORTOISE correspond to what is described in the paper "Chang, Lin‐Ching, Lindsay Walker, and Carlo Pierpaoli. "Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts." Magnetic resonance in medicine 68.5 (2012): 1654-1663”?
  • At the moment, I already have a pre-processed DWI subjID_prep.nii as well as bvals, bvecs and a pre-computed mask brain_mask.nii. Is it correct to apply the RESTORE algorithm in the following way:

            ImportNIFTI -i  ../subjID_prep.nii -b subjID.bval -v subjID.bvec -p vertical

            EstimateTensorNLLSRESTORE  -i ../subjID_proc/subjID.list -m ../brain_mask.nii

If this is the correct the way to go, 

  • What is the signal standard deviation used for performing RESTORE? Is it the same approach as in the paper mentioned above?
  • What do the following outputs exactly represent?

  1. ../subjID_R1_DT.nii: to my understanding, this is the Diffusion Tensor (Dxx, Dyy, Dzz, …). But what is the unit of measurement in which the data is stored?
  2. ../subjID_R1_OUT.nii
  3. ../subjID_R1_VOUT.nii —> it looks like the outlier map but why isn’t it binary (0-1)?
  4. ../subjID_R1_AM.nii

Thanks a lot for your precious help, looking forward to your reply!

Vincenzo Anania