  This paper proposes a novel and fast self-supervised solution for sparse-view
CBCT reconstruction (Cone Beam Computed Tomography) that requires no external
training data. Specifically, the desired attenuation coefficients are
represented as a continuous function of 3D spatial coordinates, parameterized
by a fully-connected deep neural network. We synthesize projections discretely
and train the network by minimizing the error between real and synthesized
projections. A learning-based encoder entailing hash coding is adopted to help
the network capture high-frequency details. This encoder outperforms the
commonly used frequency-domain encoder in terms of having higher performance
and efficiency, because it exploits the smoothness and sparsity of human
organs. Experiments have been conducted on both human organ and phantom
datasets. The proposed method achieves state-of-the-art accuracy and spends
reasonably short computation time.
