  In inertial confinement fusion (ICF), X-ray radiography is a critical
diagnostic for measuring implosion dynamics, which contains rich 3D
information. Traditional methods for reconstructing 3D volumes from 2D
radiographs, such as filtered backprojection, require radiographs from at least
two different angles or lines of sight (LOS). In ICF experiments, space for
diagnostics is limited and cameras that can operate on the fast timescales are
expensive to implement, limiting the number of projections that can be
acquired. To improve the imaging quality as a result of this limitation,
convolutional neural networks (CNN) have recently been shown to be capable of
producing 3D models from visible light images or medical X-ray images rendered
by volumetric computed tomography LOS (SLOS). We propose a CNN to reconstruct
3D ICF spherical shells from single radiographs. We also examine sensitivity of
the 3D reconstruction to different illumination models using preprocessing
techniques such as pseudo-flat fielding. To resolve the issue of the lack of 3D
supervision, we show that training the CNN utilizing synthetic radiographs
produced by known simulation methods allows for reconstruction of experimental
data as long as the experimental data is similar to the synthetic data. We also
show that the CNN allows for 3D reconstruction of shells that possess low mode
asymmetries. Further comparisons of the 3D reconstructions with direct multiple
LOS measurements are justified.
