A Bayesian approach for suppression of limited angular sampling artifacts in single particle 3D reconstruction

Moriya, Toshio; Acar, Erman; Cheng, R. Holland; Ruotsalainen, Ulla

doi:10.1016/j.jsb.2015.07.007

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…The filter preserves the edges in the spatial domain while using this information. The reader may refer to the previous studies for the detailed analysis of gap filling capability of sMAPEM in comparison with the conventional reconstruction methods [12,13]. One of the major drawbacks of sMAPEM is that it requires a large number of iterations for gap filling.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive multiresolution method for MAP reconstruction in electron tomography

2016

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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.

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Section: Discussionmentioning

confidence: 99%

“…Sequential maximum a posteriori expectation maximization (sMAPEM), was introduced to ET recently to compensate for the missing wedge effects [12,13]. The method assumes Poisson distribution to model the image to be reconstructed and median filter (median root prior, [11]) to regularize the iterations.…”

Section: Sequential Mapem Reconstruction Methodsmentioning

confidence: 99%

Adaptive multiresolution method for MAP reconstruction in electron tomography

2016

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“…Assume that a centered Laplace distribution of standard deviation σ (

) results in

and ϕ ( x ) = | x | + C with G being an l 1 -norm (instead of an l 2 -norm as in the case of the Gaussian prior). In an EM setup, Moriya et al [ 74 ] assumed a Median Root Prior which favors locally monotonic reconstructions.…”

Section: Sparse 3d Reconstructionsmentioning

confidence: 99%

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy

Sorzano

Vargas

Otón

et al. 2017

BioMed Research International

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One of the key steps in Electron Microscopy is the tomographic reconstruction of a three-dimensional (3D) map of the specimen being studied from a set of two-dimensional (2D) projections acquired at the microscope. This tomographic reconstruction may be performed with different reconstruction algorithms that can be grouped into several large families: direct Fourier inversion methods, back-projection methods, Radon methods, or iterative algorithms. In this review, we focus on the latter family of algorithms, explaining the mathematical rationale behind the different algorithms in this family as they have been introduced in the field of Electron Microscopy. We cover their use in Single Particle Analysis (SPA) as well as in Electron Tomography (ET).

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“…Some iterative procedures have exploited sparserepresentation of the reconstructed volume. For instance, Moriya et al[103]…”

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confidence: 99%

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Vilas

Tabassum

Mota

et al. 2018

Current Opinion in Structural Biology

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A Bayesian approach for suppression of limited angular sampling artifacts in single particle 3D reconstruction

Cited by 7 publications

References 36 publications

Adaptive multiresolution method for MAP reconstruction in electron tomography

Adaptive multiresolution method for MAP reconstruction in electron tomography

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

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