Aims: clinical radiographic imaging is seated upon the
principle of differential keV photon transmission through an
object. At clinical x-ray energies the scattering of photons causes
signal noise and is utilized solely for transmission
measurements. However, scatter — particularly Compton scatter, is
characterizable. In this work we hypothesized that modern radiation
sources and detectors paired with deep learning techniques can use
scattered photon information constructively to resolve superimposed
attenuators in planar x-ray imaging. Methods: we simulated a
monoenergetic x-ray imaging system consisting of a pencil beam x-ray
source directed at an imaging target positioned in front of a high
spatial- and energy-resolution detector array. The setup maximizes
information capture of transmitted photons by measuring off-axis
scatter location and energy. The signal was analyzed by a
convolutional neural network, and a description of scattering
material along the axis of the beam was derived. The system was
virtually designed/tested using Monte Carlo processing of simple
phantoms consisting of 10 pseudo-randomly stacked air/bone/water
materials, and the network was trained by solving a classification
problem. RESULTS: from our simulations we were able to resolve
traversed material depth information to a high degree, within our
simple imaging task. The average accuracy of the material
identification along the beam was 0.91 ± 0.01, with slightly
higher accuracy towards the entrance/exit peripheral surfaces of the
object. The average sensitivity and specificity was 0.91 and 0.95,
respectively. Conclusions: our work provides proof of principle that
deep learning techniques can be used to analyze scattered photon
patterns which can constructively contribute to the information
content in radiography, here used to infer depth information in a
traditional 2D planar setup. This principle, and our results,
demonstrate that there is information in Compton scattered photons,
and this may provide a basis for further development. The work was
limited by simple testing scenarios and without yet integrating
complexities or optimizations. The ability to scale performance to
the clinic remains unexplored and requires further study.