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

TL;DR

This paper introduces APRF, a self-supervised method for sparse-view CT reconstruction that reduces aliasing artifacts by modeling continuous relationships between projection views, resulting in higher quality images from limited data.

Contribution

The paper proposes APRF, a novel implicit neural representation that leverages spatial constraints to synthesize dense sinograms from sparse measurements, improving image quality in SVCT.

Findings

APRF outperforms state-of-the-art methods in experiments.

It produces CT images with more accurate details.

It results in fewer artifacts in reconstructed images.

Abstract

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.