  In this paper, we introduce silhouette tomography, a novel formulation of
X-ray computed tomography that relies only on the geometry of the imaging
system. We formulate silhouette tomography mathematically and provide a simple
method for obtaining a particular solution to the problem, assuming that any
solution exists. We then propose a supervised reconstruction approach that uses
a deep neural network to solve the silhouette tomography problem. We present
experimental results on a synthetic dataset that demonstrate the effectiveness
of the proposed method.
