  X-ray imaging is the most popular medical imaging technology. While x-ray
radiography is rather cost-effective, tissue structures are superimposed along
the x-ray paths. On the other hand, computed tomography (CT) reconstructs
internal structures but CT increases radiation dose, is complicated and
expensive. Here we propose "x-ray dissectography" to extract a target
organ/tissue digitally from few radiographic projections for stereographic and
tomographic analysis in the deep learning framework. As an exemplary
embodiment, we propose a general X-ray dissectography network, a dedicated
X-ray stereotography network, and the X-ray imaging systems to implement these
functionalities. Our experiments show that x-ray stereography can be achieved
of an isolated organ such as the lungs in this case, suggesting the feasibility
of transforming conventional radiographic reading to the stereographic
examination of the isolated organ, which potentially allows higher sensitivity
and specificity, and even tomographic visualization of the target. With further
improvements, x-ray dissectography promises to be a new x-ray imaging modality
for CT-grade diagnosis at radiation dose and system cost comparable to that of
radiographic or tomosynthetic imaging.
