MRI registration question

I am working on MRI image registration. I have two stack MRI sequences with different pixels per mm and different voxel sizes. I understand to register these images I have to resample these images such that they both have the same number of pixels per mm and the same voxel size. But after the resampling, I am getting a stack of blank images.I used SimpleITK for resampling. Note: I am just starting image processing and have very little experience in Simpleitk.

import SimpleITK as sitk
# Load the image
image = sitk.ReadImage('/path/to/my/mri/image.nii')

# Current spacing
original_spacing = image.GetSpacing()
print("Original spacing:", original_spacing)

# Desired new spacing - for example, [1.0, 1.0, 1.0]
new_spacing = [1.0, 1.0, 1.0]

# Calculate the new shape
original_size = image.GetSize()
new_size = [int(round(osz*osp/nsp)) for osz, osp, nsp in zip(original_size, 
original_spacing, new_spacing)]

# Set up the resample filter with the new spacing and size
resample_filter = sitk.ResampleImageFilter()

# Apply the resampling
resampled_image = resample_filter.Execute(image)

# Save the resampled image
sitk.WriteImage(resampled_image, '/path/to/my/resampled_image.nii')

Hi @Hamza_Abbasi , welcome to Neurostars! I hope you will enjoy this community!

For your questions, I don’t think that you have to bother explicitly about the resampling before the registration. Most of neuroimaging software providing registration tools take care of this resampling internally.

For instance a simple registration call with FSL would be:

flirt -in image1.nii.gz -ref image2.nii.gz -out image1_to_image2.nii.gz

And the output image would the the image1, registered to the image2, at the resolution of image2.

This is just an example. You can also do the same thing with ANTs, SPM, Nifty_reg, AFNI …

If you specifically want to use SimpleITK, here is a code that ChatGPT gave me when I asked:

import nibabel as nib
import SimpleITK as sitk

# Load the NIfTI images
image1_path = 'path/to/image1.nii.gz'
image2_path = 'path/to/image2.nii.gz'

image1 = nib.load(image1_path)
image2 = nib.load(image2_path)

# Convert NIfTI images to SimpleITK images
sitk_image1 = sitk.GetImageFromArray(image1.get_fdata())
sitk_image2 = sitk.GetImageFromArray(image2.get_fdata())

# Set up the registration method
registration_method = sitk.ImageRegistrationMethod()

# Set the similarity metric (mean squares in this case)

# Set the optimizer (gradient descent in this case)
registration_method.SetOptimizerAsRegularStepGradientDescent(learningRate=0.1, minStep=1e-4, numberOfIterations=100)

# Set the interpolator (linear in this case)

# Set the transform type (affine in this case)
transform_type = sitk.TranslationTransform(3)

# Execute the registration
transform = registration_method.Execute(sitk_image1, sitk_image2)

# Apply the transform to image2
registered_image2 = sitk.Resample(sitk_image2, sitk_image1, transform, sitk.sitkLinear, 0.0, sitk.sitkFloat32)

# Convert the registered image back to NIfTI format
registered_nifti = nib.Nifti1Image(sitk.GetArrayFromImage(registered_image2), image1.affine)

# Save the registered NIfTI image, 'path/to/registered_image2.nii.gz')