[skwb]

What advantage does dicom-numpy offer? I've mostly developed with pydicom for my medical imaging pipeline as it allows me to retain important dicom information (pixel spacing, etc). In fact, the 'PixelArray' attribute returns a numpy matrix that I can then use.

[rstevens24]

dicom-numpy's biggest advantage is that it combines individual slices into a single 3D numpy volume. This makes it really easy to immediately jump in to performing operations at the volume level rather than the slice level. It also performs some sanity checks for you, such as checking for missing slices or uneven slice spacing.

For me, I've also found dicom-numpy useful for returning the ijk-to-xyz affine transformation matrix, which describes how the voxels are oriented in patient coordinate space. dicom-numpy builds on top of PyDicom, so they are definitely not mutually exclusive! We use them both extensively.

[skwb]

Does it handle cine images easily?