How to get oblique 2d slice from volume (simpleITK)


I’m searching for code example of oblique 2d slice.
Asking chatgpt and bard I was able to understand the underlying geometry: plane center point, plane normal vector, rotation matrix, translation matrix. They even generated some code, but using methods that don’t exist.

From what I understand, the resample receives these parameters and rotates the entire volume so that I can extract it on some axis. I have already managed to extract the slices on the axial, coronal and sagittal axis, it works correctly. But now I find myself lost on how to use the resample. I read the documentation but it’s still unclear.

Please, if some code example is provided i would be very grateful.

Hello @Salu_Ramos,

This isn’t trivial because of the need to specify the resampling grid. You need to decide on its origin, size and spacing so these make sense for your use case. Resampling does not “rotate the entire volume”, it is a point based operation. Points in space are transformed and the intensity values at those spatial locations in the original image are estimated (for additional details see this notebook).

You can work on a volume to obtain an oblique version of it, multiple 2D slices in one go (this is just “hiding” the fact that by default we use the same origin, size, spacing as the original volume):

import SimpleITK as sitk

file_name = 

image = sitk.ReadImage(file_name)
image_center = image.TransformContinuousIndexToPhysicalPoint(
    [(i - 1) / 2.0 for i in image.GetSize()]
#rotate 45 degrees around x axis, center of rotation is the image center
transform = sitk.Euler3DTransform(image_center, 0.785398, 0, 0)

#use the original image grid for resampling.
sitk.WriteImage(sitk.Resample(image, transform), "oblique.nrrd")
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Hello @zivy,

thank you very much for your reply.
i made changes to the code and my function looked like this, for future readers:

import numpy as np
import SimpleITK as sitk
import time

def oblique_slice(self, theta_x_degrees:float, theta_y_degrees:float, theta_z_degrees:float, slice_size:list[int], slice_index:list[int]) -> np.array:
        extrai um slice obliquo do volume e retorna em formato de array numpy
        theta_x_radians = np.deg2rad(theta_x_degrees)
        theta_y_radians = np.deg2rad(theta_y_degrees)
        theta_z_radians = np.deg2rad(theta_z_degrees)
        rotation_matrix = sitk.Euler3DTransform()
        rotation_matrix.SetRotation(theta_x_radians, theta_y_radians, theta_z_radians)
        time1 = time.time()
        rotated_volume = sitk.Resample(self.volume, rotation_matrix)
        time2 = time.time()
        print(f"resample timer = {time2 - time1}")
        slice_extraction = sitk.Extract(rotated_volume, slice_size, slice_index)
        return sitk.GetArrayFromImage(slice_extraction)

With this parrameters i will be able to implement mpr function.
I am trying to optimize this code because Resample are taking too much time, approximately 0.04/0.045 seconds, which gives me 20 frames per second.
In my MainSettings i set:


And now i’m trying to use gpu with pytorch or tensorflow, after reading this:

which by coincidence you answered too, lol!
I will bring news in case I get something.

Hello @Salu_Ramos,

I am not sure the size of the input image, but you are resampling the whole input image, then extracting a slice. It would improve efficiency when resampling if the output image size was specifies to only have a size of 1 in the z dimensions.

1 Like

Hi @blowekamp,

i believe you are correct, it turns out that python was not giving good overall performance expectations for the project I am working on (dicom viewer), neither extending python with c or using multithreadings would solve it. So I converted the project to Java and as a result I ended up not bringing any performance upgrade to my code above.