Interpolate/Resize 3D array

Question

I have a 3D array holding voxels from a mri dataset. The model could be stretched along one or more directions. E.g. the voxel size (x,y,z) could be 0.5x0.5x2 mm. Now I want to

Answer 1

You can also use PyTorch + TorchIO. The following is the resampling of a 3D array with gaussian interpolation (the zoom function is adapted from the code that my supervisor wrote for me):

def zoom(
    label_stack: Sequence[Sequence[SupportsFloat]],
    spacing: Sequence[Union[SupportsInt, SupportsFloat]],
) -> np.ndarray:
    spacing = np.asanyarray(spacing)
    target = tuple(1 / spacing)

    # Resample the array with respect to spacing parameters
    arr4d = np.expand_dims(label_stack, 0)
    transform = tio.Resample(target, image_interpolation="gaussian")
    transformed = transform(arr4d)
    result = np.squeeze(transformed)

    # Perform an arbitrary gaussian to smoothen the surface.
    # Achieves a more smooth surface
    sigma = (round(spacing[0] * 0.25), 7, 7)
    gaussian_3d = gaussian_filter(result, sigma=sigma)

    # Binary thresholding
    highpass_threshold = np.median(gaussian_3d[gaussian_3d > 0.09])
    thresh = threshold_array(gaussian_3d, highpass=highpass_threshold)

    return thresh


def threshold_array(
    array: np.ndarray,
    lowpass: Union[Union[SupportsInt, SupportsFloat], None] = None,
    highpass: Union[Union[SupportsInt, SupportsFloat], None] = None,
) -> np.ndarray:
    if lowpass and highpass:
        msg = "Defition of both lowpass and highpass is illogical"
        raise ValueError(msg)

    if not lowpass and not highpass:
        msg = "Either lowpass or highpass has to be defined"
        raise ValueError(msg)

    array = np.asanyarray(array)
    filtered = np.zeros_like(array, dtype=np.int8)

    if lowpass:
        filtered[array < lowpass] = 1
    if highpass:
        filtered[array > highpass] = 1

    return filtered

The images array is z-x-y, and zoom has been designed accordingly.

Spacing is defined by the user. Spacing has to be a sequence that has the same length as the shape of the image numpy array. The interpolated image will have the shape as spacing * "old shape".

I wish there was cubic method for the interpolation in the Resample function. I tried with gaussian + thresholding, but haven't gotten any great results. Better than whatever I could muster with SciPy though.

Please credit when used.