Hello @isuruwi,

In my recommendation I assumed you had already computed the deformation field which mapped points from the original mesh extracted from the image to the new mesh, apparently that is not the situation.

Below is pseudo-code based on the current understanding:

```
# Step 1 => Define a new image grid which bounds the deformed mesh points. This may not match the original image. If it does, then
# this step can be skipped and transformation_domain=reference_image
#Note that if the image is much larger than the mesh you should compute the transformation_domain
predicted_deformed_mesh = meshio.read('./deformed_MCR.vtk').points
# Get the minimum/maximum coordinates that bound the deformed mesh and compute the physical
# size of this axis aligned bounding box, add some padding.
min_x,max_x,min_y,max_y,min_z,max_z = bounding_box(predicted_deformed_mesh)
phys_size = [pad+max_x-min_x, pad+max_y-min_y, pad+max_z-min_z]
# define the new image grid
new_origin = [min_x-pad/2.0, min_y-pad/2.0, min_z-pad/2.0]
new_spacing = reference_image.GetSpacing() # or we can set any new arbitrary spacing
new_size = [ int(round(psz/spc)) for psz, spc in zip(phys_size, new_spacing)]
transformation_domain = sitk.Image(new_size, sitk.sitkUInt8)
transformation_domain.SetOrigin(new_origin)
transformation_domain.SetSpacing(new_spacing)
# Step 2 => Obtain the (approximate) BSpline/FFD transformation that maps the points from the deformed mesh to the original
ref_mesh = meshio.read('./MCR.vtk').points
landmark_transform_initializer_filter = LandmarkBasedTransformInitializerFilter()
landmark_transform_initializer_filter.SetFixedLandmarks(predicted_deformed_mesh_points_flattened) # points in flattened list [x1,y1,z1,x2,y2,z2...]
landmark_transform_initializer_filter.SetMovingLandmarks(ref_mesh_points_flattened) # points in flattened list [x1,y1,z1,x2,y2,z2...]
landmark_transform_initializer_filter.SetBSplineNumberOfControlPoints(num_control_points) # more control points the better the fit, be careful to not over-fit.
landmark_transform_initializer_filter.SetReferenceImage(transformation_domain)
dimension=3
spline_order=3
bspline_transform = sitk.BSplineTransform(dimension, spline_order)
bspline_transform = landmark_transform_initializer_filter.Execute(bspline_transform)
# Step 3 => Resample
resampled_image = sitk.Resample(reference_image, transformation_domain, bspline_transform)
```

One last, Python related comment, use variable unpacking.

```
# Don't do
x = reference_image.GetSize()[0]
y = reference_image.GetSize()[1]
z = reference_image.GetSize()[2]
# Do
x, y, z = reference_image.GetSize()
```