  Stitching images acquired under perspective projective geometry is a relevant
topic in computer vision with multiple applications ranging from smartphone
panoramas to the construction of digital maps. Image stitching is an equally
prominent challenge in medical imaging, where the limited field-of-view
captured by single images prohibits holistic analysis of patient anatomy. The
barrier that prevents straight-forward mosaicing of 2D images is depth mismatch
due to parallax. In this work, we leverage the Fourier slice theorem to
aggregate information from multiple transmission images in parallax-free
domains using fundamental principles of X-ray image formation. The semantics of
the stitched image are restored using a novel deep learning strategy that
exploits similarity measures designed around frequency, as well as dense and
sparse spatial image content. Our pipeline, not only stitches images, but also
provides orthographic reconstruction that enables metric measurements of
clinically relevant quantities directly on the 2D image plane.
