  Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse
problem in imaging that aims to acquire high-quality CT images based on
sparsely-sampled measurements. Recent works use Implicit Neural Representations
(INRs) to build the coordinate-based mapping between sinograms and CT images.
However, these methods have not considered the correlation between adjacent
projection views, resulting in aliasing artifacts on SV sinograms. To address
this issue, we propose a self-supervised SVCT reconstruction method --
Anti-Aliasing Projection Representation Field (APRF), which can build the
continuous representation between adjacent projection views via the spatial
constraints. Specifically, APRF only needs SV sinograms for training, which
first employs a line-segment sampling module to estimate the distribution of
projection views in a local region, and then synthesizes the corresponding
sinogram values using center-based line integral module. After training APRF on
a single SV sinogram itself, it can synthesize the corresponding dense-view
(DV) sinogram with consistent continuity. High-quality CT images can be
obtained by applying re-projection techniques on the predicted DV sinograms.
Extensive experiments on CT images demonstrate that APRF outperforms
state-of-the-art methods, yielding more accurate details and fewer artifacts.
Our code will be publicly available soon.
