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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): 16541663”?
 At the moment, I already have a preprocessed DWI subjID_prep.nii as well as bvals, bvecs and a precomputed 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?
 ../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?
 ../subjID_R1_OUT.nii
 ../subjID_R1_VOUT.nii —> it looks like the outlier map but why isn’t it binary (01)?
 ../subjID_R1_AM.nii
Thanks a lot for your precious help, looking forward to your reply!
Vincenzo Anania