Computational Analysis and Improvement of SIRT

Gregor, Jens; Benson, Thomas

doi:10.1109/tmi.2008.923696

Cited by 169 publications

(134 citation statements)

References 21 publications

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“…(3) SIRT (Simultaneous Iterative Reconstruction Technique) is an algebraic technique that computes a weighted least squares solution of (1) [13]. SIRT was implemented as defined in [14], performing 300 iterations. (4) PDART [15] (Partially Discrete Algebraic Reconstruction Technique) is a partially discrete technique that assumes that only the densest material is homogeneous.…”

Section: Experiments and Resultsmentioning

confidence: 99%

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Roelandts

Batenburg

Dekker

et al. 2014

Computer Vision and Image Understanding

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a b s t r a c tIn this paper, we present the reconstructed residual error, which evaluates the quality of a given segmentation of a reconstructed image in tomography. This novel evaluation method, which is independent of the methods that were used to reconstruct and segment the image, is applicable to segmentations that are based on the density of the scanned object. It provides a spatial map of the errors in the segmented image, based on the projection data. The reconstructed residual error is a reconstruction of the difference between the recorded data and the forward projection of that segmented image. The properties and applications of the algorithm are verified experimentally through simulations and experimental micro-CT data. The experiments show that the reconstructed residual error is close to the true error, that it can improve gray level estimates, and that it can help discriminating between different segmentations.

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Section: Experiments and Resultsmentioning

confidence: 99%

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Roelandts

Batenburg

Dekker

et al. 2014

Computer Vision and Image Understanding

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“…1 (a), an example of the cupping artifact is given. It is a 2D cross section of an Au-Ag core-shell nanoparticle [7] , reconstructed using the SIRT algorithm [8] . If we look at the line-profile of the 2D image ( Fig.…”

Section: E-mail Address: Zhong@cwinl (Z Zhong)mentioning

confidence: 99%

“…The reconstruction is computed using the simultaneous iterative reconstruction technique (SIRT) [8] which solves the following least-squares problem:…”

Section: The Correction Algorithmmentioning

confidence: 99%

Automatic correction of nonlinear damping effects in HAADF–STEM tomography for nanomaterials of discrete compositions

et al. 2018

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a b s t r a c t HAADF-STEM tomography is a common technique for characterizing the three-dimensional morphology of nanomaterials. In conventional tomographic reconstruction algorithms, the image intensity is assumed to be a linear projection of a physical property of the specimen. However, this assumption of linearity is not completely valid due to the nonlinear damping of signal intensities. The nonlinear damping effects increase w.r.t the specimen thickness and lead to so-called "cupping artifacts", due to a mismatch with the linear model used in the reconstruction algorithm. Moreover, nonlinear damping effects can strongly limit the applicability of advanced reconstruction approaches such as Total Variation Minimization and discrete tomography.In this paper, we propose an algorithm for automatically correcting the nonlinear effects and the subsequent cupping artifacts. It is applicable to samples in which chemical compositions can be segmented based on image gray levels. The correction is realized by iteratively estimating the nonlinear relationship between projection intensity and sample thickness, based on which the projections are linearized. The correction and reconstruction algorithms are tested on simulated and experimental data.

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“…In our implementation, the basic algorithm for computing a real-valued surrogate solution is the iterative SIRT algorithm [18], defined as follows. Let…”

Section: Surrogate Solutionsmentioning

confidence: 99%

“…It can be shown that for a consistent system of equations, the SIRT-algorithm as described in Eq. (6) converges to the solutionṽ that is closest to the initial image v (0) with respect to a certain norm (a weighted sum of squares, see [18]). Here, we use an adaptation of this algorithm which confines the solution to the interval [0, 1].…”

Section: Surrogate Solutionsmentioning

confidence: 99%

Dynamic angle selection in binary tomography

Batenburg

Palenstijn

Balázs

et al. 2013

Computer Vision and Image Understanding

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a b s t r a c tIn this paper, we present an algorithm for the dynamic selection of projection angles in binary tomography. Based on the information present in projections that have already been measured, a new projection angle is computed, which aims to maximize the information gained by adding this projection to the set of measurements. The optimization model used for angle selection is based on a characterization of solutions of the binary reconstruction problem, and a related definition of information gain. From this formal model, an algorithm is obtained by several approximation steps. Results from a series of simulation experiments demonstrate that the proposed angle selection scheme is indeed capable of finding angles for which the reconstructed image is much more accurate than for the standard angle selection scheme.

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Computational Analysis and Improvement of SIRT

Cited by 169 publications

References 21 publications

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography

Automatic correction of nonlinear damping effects in HAADF–STEM tomography for nanomaterials of discrete compositions

Dynamic angle selection in binary tomography

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