[Neuroimaging] [Dipy] discrepancies of SH coeffs with MRtrix

[Eric Moulton]

Hi everyone,

Thanks to all those who answered me. It cleared a lot of stuff up for me. I
spent some time playing with these functions to get the hang of it. Indeed,
I performed the ConstrainedSphericalDeconvModel model and it does give me a
spatially varying Y(0,0) volume, just like in MRtrix.

That being said, I tried computing the SH_coeff volumes using similar
algorithms in MRtrix and Dipy, and although I get close results, I do not
get the same. From my understanding, Dipy seems to employ the Tax algorithm
for calculating the response function, so that's what I tried to do in
MRtrix. I started out with a 4D Dwi file (eddy corrected, etc.), a brain
mask, and the bvecs and bvals.

The parts of my code in bold are the parts where I tried to put in the
exact same parameters for each program. The only extra options that could
be responsible for the differences are in Dipy, where I have to give an
initial FA and MD value.

*************************
*MRtrix Code*
mrconvert dwi.nii.gz dwi.mif
mrconvert dwi_mask.nii.gz dwi_mask.mif
dwi2response tax *-peak_ratio 0.01 -max_iters 8 -convergence 0.001 -lmax 4
-mask dwi_mask.mif* -fslgrad subj.bvecs subj.bvals -force dwi.mif
response_tax.txt
dwi2fod -fslgrad subj.bvecs subj.bvals -lmax 4 -mask mask.mif -force csd
dwi.mif response_tax.txt odf_tax.mif

*Dipy Code*
import numpy as np
import nibabel as nib
import os
from dipy.data import get_sphere
from dipy.reconst.shm import CsaOdfModel,
from dipy.direction import peaks_from_model
from dipy.tracking import utils
from dipy.reconst.csdeconv import recursive_response
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel

base_rep='/path/to/files'
fimg = os.path.join(base_rep,'dwi.nii.gz')
fbvecs = os.path.join(base_rep,'subj.bvecs')
fbvals = os.path.join(base_rep,'subj.bvals')
img = nib.load(fimg)
dwi = img.get_data()
fdwi_mask = os.path.join(base_rep,'dwi_mask.nii.gz')
dwi_mask = nib.load(fdwi_mask).get_data()
dwi_mask = dwi_mask > 0
affine = img.affine
header = img.header
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
sphere = get_sphere('symmetric724')

response = recursive_response(gtab,dwi,*mask=dwi_mask*,sh_order=4,
                              *peak_thr=0.01,iter=8,convergence = 0.001,*
                              init_fa=0.05,init_trace=0.0021,
                              parallel=True,sphere=sphere)

csdmodel = ConstrainedSphericalDeconvModel(gtab,response,sh_order=4)
SH_coeff = csdmodel.fit(dwi,mask=dwi_mask).shm_coeff

#some trickery to put the coefficients in the same order as MRtrix
SH_coeff_mrtrix = np.zeros(SH_coeff.shape)
SH_coeff_mrtrix[...,0] = SH_coeff[...,0]
SH_coeff_mrtrix[...,1:6] = SH_coeff[...,5:0:-1]
SH_coeff_mrtrix[...,6:15] = SH_coeff[...,14:5:-1]

nib.save(nib.Nifti1Image(SH_coeff_mrtrix,affine,header),
os.path.join(base_rep,'odf_tax_dipy.nii.gz'))
*************************


When I open odf_tax.mif and odf_tax_dipy.nii.gz in mrview, I don't get the
exact same thing, but both volumes are fairly similar upon visual
inspection of each component and also the ODFs (although in areas of
crossing fibers, they can sometimes be more or less "sharper", but
sometimes MRtrix OR Dipy produces the sharper one depending on the
location).

Attached is a histogram of the differences of the images (MRtrix - Dipy),
masked by the brain_mask so we don't get any 0 voxels outside the brain.
Each component is the Y(l,m) volumes in MRtrix3 basis (i.e. 0th = Y(0,0),
1st = Y(2,-2), 2nd = Y(2,-1), etc.).

I was wondering what you guys thought about my code to generate the
spherical deconvolutions and if these differences are worrying. Also, is it
unrealistic to hope to get the exact same result using two different
softwares with lots of behind the scenes calculations?

Best regards,
Eric
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/neuroimaging/attachments/20161025/5d7da24d/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: mrtrix-dipy_lmax4.png
Type: image/png
Size: 164416 bytes
Desc: not available
URL: <http://mail.python.org/pipermail/neuroimaging/attachments/20161025/5d7da24d/attachment-0001.png>