I'm often asked how to go from voxel and mm coordinates using Python. This can be easily achieved using nibabel package with only few lines of code. The following tutorial is based on +Matthew Brett answer on the nipy mailing list.
import nibabel as nib
Load the NIFTI file defining the space you are interested in. For the purpose of this tutorial we will use a test dataset shipped with nibabel.
data_fname = os.path.join(os.path.dirname(nib.__file__), 'tests', 'data', 'example4d.nii.gz')
img = nib.load(data_fname)
Get the affine matrix and convert the coordinates.
aff = img.get_affine()
real_pt = nib.affines.apply_affine(aff, [22, 34, 12])
real_pt
array([ 73.85510254, 27.1169095 , 29.79324198])
import numpy.linalg as npl
nib.affines.apply_affine(npl.inv(aff), real_pt)
array([ 22., 34., 12.])
Going from voxel to mm coordinates
import osimport nibabel as nib
Load the NIFTI file defining the space you are interested in. For the purpose of this tutorial we will use a test dataset shipped with nibabel.
data_fname = os.path.join(os.path.dirname(nib.__file__), 'tests', 'data', 'example4d.nii.gz')
img = nib.load(data_fname)
Get the affine matrix and convert the coordinates.
aff = img.get_affine()
real_pt = nib.affines.apply_affine(aff, [22, 34, 12])
real_pt
array([ 73.85510254, 27.1169095 , 29.79324198])
Going from mm to voxel coordinates
Going the other direction is even easier.import numpy.linalg as npl
nib.affines.apply_affine(npl.inv(aff), real_pt)
array([ 22., 34., 12.])
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Great explanation! Converting between voxel and millimeter units is often overlooked in neuroimaging workflows, so it’s fantastic that you’ve covered this topic. Your step-by-step guide makes the process much clearer. This is an invaluable resource for researchers working with voxel-based data. Keep up the awesome work!
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Converting between voxel and mm coordinates in Python is essential for accurate spatial data processing. This method provides a clear approach to map voxel data to real-world measurements efficiently!
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Data science courses in Mumbai
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