  X-ray radiography is the most readily available imaging modality and has a
broad range of applications that spans from diagnosis to intra-operative
guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation
of the hidden and obscured anatomy in X-ray images remains a challenge and
often requires high radiation dose and imaging from several perspectives. In
this work, we aim at decomposing the conventional X-ray image into d X-ray
components of independent, non-overlapped, clipped sub-volumes using deep
learning approach. Despite the challenging aspects of modeling such a highly
ill-posed problem, exciting and encouraging results are obtained paving the
path for further contributions in this direction.
