[Neuroimaging] Using RESTORE in a dti_model fit

Perea Camargo, Rodrigo Dennis RPEREACAMARGO at mgh.harvard.edu
Thu Dec 1 19:23:07 EST 2016

Hi all,
I am trying to reconstruct a diffusion tensor model while correcting for outliers using RESTORE. In the Dipy documentation I found this helpful link:
and I follow some instructions creating a script that I copied shown below.

The problem is that 1) I am new at Python and 2) new to Dipy. Apparently the link provides a comparison example for introduced noisy data but it is not very specific for running  RESTORE. Could anybody provide me more advice? My goal is to apply RESTORE to my DWIs and extract/save the FA (and other ) diffusivity  maps,  but I got stuck at the end…..

Thanks in advance,

HERE IS MY SCRIPT (check below for my questions, if not clearer):


#import modules
import numpy as np
import nibabel as nib
from os.path import expanduser, join

#data initialization

#loading nii
data = img.get_data()

#loading bvecs/bvals
from dipy.io<http://dipy.io> import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)

#Creating the gradient table
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)

#Reconstructing a model
dti_wls = dti.TensorModel(gtab)

#Estimate noise (or sigma) for restore
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)

dti_restore = dti.TensorModel(gtab,fit_method='RESTORE', sigma=sigma)

#Now how do I get my tensor outputs??
#I believe I haven’t even apply the tensor fit…. not sure what else to do.

Rodrigo Dennis Perea
Research Fellow
Department of Radiology
Athinoula A. Martinos Center
Massachusetts General Hospital
Harvard Medical School
rpereacamargo at mgh.harvard.edu<mailto:rpereacamargo at mgh.harvard.edu>

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