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Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverseproblem in imaging that aims to acquire high-quality CT images based onsparsely-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 adjacentprojection views, resulting in aliasing artifacts on SV sinograms. To addressthis issue, we propose a self-supervised SVCT reconstruction method --Anti-Aliasing Projection Representation Field (APRF), which can build thecontinuous representation between adjacent projection views via the spatialconstraints. Specifically, APRF only needs SV sinograms for training, whichfirst employs a line-segment sampling module to estimate the distribution ofprojection views in a local region, and then synthesizes the correspondingsinogram values using center-based line integral module. After training APRF ona single SV sinogram itself, it can synthesize the corresponding dense-view(DV) sinogram with consistent continuity. High-quality CT images can beobtained by applying re-projection techniques on the predicted DV sinograms.Extensive experiments on CT images demonstrate that APRF outperformsstate-of-the-art methods, yielding more accurate details and fewer artifacts.Our code will be publicly available soon.

APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging