voxel2streamline to see which streamlines pass through which voxels. I feed in the streamlines and the inverse of the DWI affine to map from streamline to voxel. I am having trouble interpreting the output, as I do not know how to map voxel index to location. Any guidance would be appreciated, thanks!
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fbvec = f'{file2_path}.bvec'
data, affine, img = load_nifti(file2_path, return_img=True)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
print(bvals)
gtab = gradient_table(bvals, bvecs)
print(gtab)
tenmodel = TensorModel(gtab)
print(len(bvals))
new_img = img.get_fdata()
tenfit = tenmodel.fit(new_img)File "main.py", line 197, in <module>
convert(args.f1t, args.f2t, args.f1p, args.f2p, args.s, args.diff)
File "main.py", line 171, in convert
tenfit = tenmodel.fit(new_img)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/dipy/reconst/dti.py", line 793, in fit
self.kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/dipy/reconst/dti.py", line 1303, in wrapped_fit_tensor
*args, kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/dipy/reconst/dti.py", line 1381, in wls_fit_tensor
w = np.exp(np.einsum('...ij,...j', ols_fit, log_s))
File "<__array_function__ internals>", line 6, in einsum
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/einsumfunc.py", line 1356, in einsum
return c_einsum(operands, *kwargs)
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (10,10)->(10,10) (10000,11)->(10000,newaxis,11)
dipy.align._public.SymmetricDiffeomorphicRegistration. Using the .update() method, I have interpolated the displacement to a new 3D shape of (S,R,C,3). I would now like to split this 3D array into a list of 2D arrays and then apply each deformation field on a new 2D image. Is there a way of doing that?
# these are your original bvals and bvecs
bvals = gtab.bvals
bvecs = gtab.bvecs
# here we select only 0 and 1000 bvals
selelected_bvals = np.logical_or(bvals == 0, bvals == 1000)
# here we pick only the 3D volumes from the 4D dMRI data pertaining to 0 and 1000 that we did above.
data_selected = data[:, :, :, selelected_bvals]
# now we also take only the bvals and bvecs of the same 0 and 1000 shells.
gtab_selected = gradient_table(bvals[selelected_bvals], bvecs[selelected_bvals])
# now you can use these to fit the DTI model
tenmodel = dti.TensorModel(gtab_selected)
tenfit = tenmodel.fit(data_selected)
Is there a project outline for dipy such as a module connections graph?
Such as these I created for fmriprep and qsiprep: https://github.com/nipreps/fmriprep/discussions/2517 and https://github.com/PennLINC/qsiprep/discussions/289
nodes and therefore does not need a dataflow graph. However, I do know it is trivial to build these pipelines with DIPY and would like to know what exactly you need. Are you using vanilla DIPY to build pipelines?
@kodiweera ! That is a great question. The graph that you are mentioning typically comes from NiPype -- which I believe is how the nodes are connected for a dataflow style of computation. DIPY on the other hand is a package that provides tools for what happens inside those
nodesand therefore does not need a dataflow graph. However, I do know it is trivial to build these pipelines with DIPY and would like to know what exactly you need. Are you using vanilla DIPY to build pipelines?
Not a nipype node grape. I was mentioning a modules connection graph. I alrady created them for fmri and qsiprep. they simply show the dependancy on the packages etc.
