Improving analytical tomographic reconstructions through consistency conditions

TL;DR

This paper presents a fast, parameterless filter based on Helgason-Ludwig consistency conditions that enhances the accuracy of analytical tomographic reconstructions from undersampled data by extrapolating additional projections.

Contribution

It introduces a novel Radon domain filter that improves analytical reconstruction quality by leveraging consistency conditions, without requiring parameter tuning.

Findings

Peak SNR improved by 5-6 dB with the filter

Filter effectively extrapolates intermediate projections

Enhances accuracy of standard analytical methods

Abstract

This work introduces and characterizes a fast parameterless filter based on the Helgason-Ludwig consistency conditions, used to improve the accuracy of analytical reconstructions of tomographic undersampled datasets. The filter, acting in the Radon domain, extrapolates intermediate projections between those existing. The resulting sinogram, doubled in views, is then reconstructed by a standard analytical method. Experiments with simulated data prove that the peak-signal-to-noise ratio of the results computed by filtered backprojection is improved up to 5-6 dB, if the filter is used prior to reconstruction.