Sparse Filtered SIRT for Electron Tomography
This addresses reconstruction challenges in electron tomography for researchers, but appears incremental as it builds on existing filtered backprojection and iterative techniques.
The paper tackles the problem of noise and missing wedges in electron tomographic reconstruction by proposing a novel filtered backprojection method that optimizes filters based on reconstruction error, and embeds it within an iterative framework. Numerical studies demonstrate performance gains over state-of-the-art methods.
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.