  Electron tomography (ET) has become a standard technique for 3D
characterization of materials at the nano-scale. Traditional reconstruction
algorithms such as weighted back projection suffer from disruptive artifacts
with insufficient projections. Popularized by compressed sensing,
sparsity-exploiting algorithms have been applied to experimental ET data and
show promise for improving reconstruction quality or reducing the total beam
dose applied to a specimen. Nevertheless, theoretical bounds for these methods
have been less explored in the context of ET applications. Here, we perform
numerical simulations to investigate performance of l_1-norm and
total-variation (TV) minimization under various imaging conditions. From 36,100
different simulated structures, our results show specimens with more complex
structures generally require more projections for exact reconstruction.
However, once sufficient data is acquired, dividing the beam dose over more
projections provides no improvements - analogous to the traditional
dose-fraction theorem. Moreover, a limited tilt range of +-75 or less can
result in distorting artifacts in sparsity-exploiting reconstructions. The
influence of optimization parameters on reconstructions is also discussed.
