  Tomographic imaging is useful for revealing the internal structure of a 3D
sample. Classical reconstruction methods treat the object of interest as a
vector to estimate its value. Such an approach, however, can be inefficient in
analyzing high-dimensional data because of the underexploration of the
underlying structure. In this work, we propose to apply a tensor-based
regression model to perform tomographic reconstruction. Furthermore, we explore
the low-rank structure embedded in the corresponding tensor form. As a result,
our proposed method efficiently reduces the dimensionality of the unknown
parameters, which is particularly beneficial for ill-posed inverse problem
suffering from insufficient data. We demonstrate the robustness of our proposed
approach on synthetic noise-free data as well as on Gaussian noise-added data.
