RAPToR: A Resampling Algorithm for Pseudo-Polar based Tomographic Reconstruction

TL;DR

This paper introduces RAPToR, a fast and stable resampling algorithm that transforms parallel-beam measurements into a pseudo-polar grid for improved tomographic reconstruction, especially under noise and limited angles.

Contribution

The paper presents a novel resampling method based on sampling theory that enhances stability and accuracy in tomographic reconstruction from parallel-beam data.

Findings

Improved reconstruction quality over state-of-the-art methods.

Effective noise reduction through measurement denoising.

Successful reconstruction from fewer angles and high noise levels.

Abstract

We propose a stable and fast reconstruction technique for parallel-beam (PB) tomographic X-ray imaging, relying on the discrete pseudo-polar (PP) Radon transform. Our main contribution is a resampling method, based on modern sampling theory, that transforms the acquired PB measurements to a PP grid. The resampling process is both fast and accurate, and in addition, simultaneously denoises the measurements, by exploiting geometrical properties of the tomographic scan. The transformed measurements are then reconstructed using an iterative solver with total variation (TV) regularization. We show that reconstructing from measurements on the PP grid, leads to improved recovery, due to the inherent stability and accuracy of the PP Radon transform, compared with the PB Radon transform. We also demonstrate recovery from a reduced number of PB acquisition angles, and high noise levels. Our approach is shown to achieve superior results over other state-of-the-art solutions, that operate directly on the given PB measurements. The proposed method can benefit fan-beam and/or cone-beam projections by coupling it with a rebinning process.