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Adaptive multiresolution method for MAP reconstruction in electron tomography

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Details

Original languageEnglish
Pages (from-to)24-34
Number of pages11
JournalUltramicroscopy
Volume170
DOIs
Publication statusPublished - 1 Nov 2016
Publication typeA1 Journal article-refereed

Abstract

3D image reconstruction with electron tomography holds problems due to the severely limited range of projection angles and low signal to noise ratio of the acquired projection images. The maximum a posteriori (MAP) reconstruction methods have been successful in compensating for the missing information and suppressing noise with their intrinsic regularization techniques. There are two major problems in MAP reconstruction methods: (1) selection of the regularization parameter that controls the balance between the data fidelity and the prior information, and (2) long computation time. One aim of this study is to provide an adaptive solution to the regularization parameter selection problem without having additional knowledge about the imaging environment and the sample. The other aim is to realize the reconstruction using sequences of resolution levels to shorten the computation time. The reconstructions were analyzed in terms of accuracy and computational efficiency using a simulated biological phantom and publically available experimental datasets of electron tomography. The numerical and visual evaluations of the experiments show that the adaptive multiresolution method can provide more accurate results than the weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), and sequential MAP expectation maximization (sMAPEM) method. The method is superior to sMAPEM also in terms of computation time and usability since it can reconstruct 3D images significantly faster without requiring any parameter to be set by the user.

Keywords

  • Adaptive reconstruction, Electron tomography (ET), Maximum a posteriori (MAP) reconstruction, Missing wedge, Multiresolution reconstruction, Regularization parameter

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Field of science, Statistics Finland