Sparse Filtered SIRT for Electron Tomography

TL;DR

This paper introduces a novel filtered backprojection method for electron tomography that adaptively optimizes filters to reduce noise and mitigate missing wedge artifacts, improving reconstruction quality.

Contribution

It proposes a data-dependent filter optimization and an embedded iterative scheme to enhance electron tomographic reconstructions under noisy and incomplete data conditions.

Findings

Significant noise reduction in reconstructions

Improved handling of missing wedge artifacts

Enhanced image quality over existing methods

Abstract

Electron tomographic reconstruction is a method for obtaining a three-dimensional image of a specimen with a series of two dimensional microscope images taken from different viewing angles. Filtered backprojection, one of the most popular tomographic reconstruction methods, does not work well under the existence of image noises and missing wedges. This paper presents a new approach to largely mitigate the effect of noises and missing wedges. We propose a novel filtered backprojection that optimizes the filter of the backprojection operator in terms of a reconstruction error. This data-dependent filter adaptively chooses the spectral domains of signals and noises, suppressing the noise frequency bands, so it is very effective in denoising. We also propose the new filtered backprojection embedded within the simultaneous iterative reconstruction iteration for mitigating the effect of missing wedges. Our numerical study is presented to show the performance gain of the proposed approach over the state-of-the-art.