Sparse filtered SIRT for electron tomography. (June 2020)

• Record Type: Journal Article
• Authors: Mu, Chen; Park, Chiwoo
• Is Part Of: Pattern recognition. Volume 102(2020:Jun.)
• Publication Date: 2020-06
• Subjects: Tomographic reconstruction -- Filtered backprojection -- Filter optimization -- Filtered backprojection within SIRT; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107253
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper presents a new filtered backprojection for tomographic reconstruction. The approach optimizes the backprojection filter to minimize the reconstruction error. The filtered backprojection can be iteratively performed as a part of SIRT. The iterative approach largely mitigates the effect of noises and missing wedges. The approach was validated using four benchmark images and one experimental image. 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.