Compressed sensing electron tomography

Leary, Rowan K.; Saghi, Zineb; Midgley, Paul A.; Holland, Daniel J.

doi:10.1016/j.ultramic.2013.03.019

Cited by 262 publications

(221 citation statements)

References 64 publications

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“…It is interesting that the edge shift is increasing with increasing tilt increments in both X and Z directions, indicating that the compressed sensing approaches is significantly influenced by the tilt increment when a limited electron dose is used. This is contrary to previous results where compressive sensing based approaches are used for the reconstruction of nanostructures based on high-angle annular dark-field STEM tilt series with a higher SNR [23,35]. As a consequence of the observed edge shifts, the size of objects will be underestimated.…”

Section: Tvmcontrasting

confidence: 90%

“…Due to too flat or chaotic behavior of the global edge profiles, in some condition the gap width cannot be determined, thus, leading to blank data points, for example, for the 3 nm gaps along the Z direction (f). S31 denotes the Saxton scheme with an angular starting interval of 31. cannot reproduce the general conclusions that compressive sensing approaches deal better with fewer projections [32,33,35]. The DART provides a binarized reconstruction and suppresses the elongation artifacts along the beam direction.…”

Section: Discussionmentioning

confidence: 93%

“…Nevertheless, some studies have incorporated known phantoms or models into the performance comparison, but these models were based on data that are obtained with a very high SNR in the projections [32,34,35]. Hence, no study has included effects of limited electron dose, which we consider the most crucial physical limit to 3D imaging of beam sensitive materials.…”

Section: Introductionmentioning

confidence: 99%

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The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections

Chen¹,

Goris

Bleichrodt

et al. 2014

Ultramicroscopy

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a b s t r a c tIn electron tomography, the fidelity of the 3D reconstruction strongly depends on the employed reconstruction algorithm. In this paper, the properties of SIRT, TVM and DART reconstructions are studied with respect to having only a limited number of electrons available for imaging and applying different angular sampling schemes. A well-defined realistic model is generated, which consists of tubular domains within a matrix having slab-geometry. Subsequently, the electron tomography workflow is simulated from calculated tilt-series over experimental effects to reconstruction. In comparison with the model, the fidelity of each reconstruction method is evaluated qualitatively and quantitatively based on global and local edge profiles and resolvable distance between particles. Results show that the performance of all reconstruction methods declines with the total electron dose. Overall, SIRT algorithm is the most stable method and insensitive to changes in angular sampling. TVM algorithm yields significantly sharper edges in the reconstruction, but the edge positions are strongly influenced by the tilt scheme and the tubular objects become thinned. The DART algorithm markedly suppresses the elongation artifacts along the beam direction and moreover segments the reconstruction which can be considered a significant advantage for quantification. Finally, no advantage of TVM and DART to deal better with fewer projections was observed.

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Section: Tvmcontrasting

confidence: 90%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections

Chen¹,

Goris

Bleichrodt

et al. 2014

Ultramicroscopy

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“…The partially discrete algebraic reconstruction technique (PDART) exploits the existence of dense homogeneous particles of which the grey value is known by incorporating this knowledge in the reconstruction algorithm, resulting in more accurate reconstruction quality [10]. Other methods minimize the total variation of the reconstruction, where the sample is assumed to have a sparse gradient, i.e., the number of boundary pixels in the sample is relatively small compared to the total number of pixels [11,12]. However, the prior knowledge assumptions incorporated in the reconstruction algorithms of the previous examples are not always applicable to nanoporous materials, since the reconstruction may consist of a continuous range of grey values with non-sparsity of the gradient image.…”

Section: Introductionmentioning

confidence: 99%

Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials

et al. 2015

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a b s t r a c tElectron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm "POre REconstruction and Segmentation" is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials.

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“…Advances made on the acquisition side, especially in electron tomography of materials [1,2], are accompanied by new computational methods for reconstruction and segmentation of volumes from a tilt series of projected images [3,4,5]. However, although methods become more and more sophisticated, still most of them require substantial user intervention to tune the parameters of the reconstruction procedure and assist algorithms in finding the correct segmentation thresholds.…”

Section: Introductionmentioning

confidence: 99%

Autonomous reconstruction and segmentation of tomographic data

Wollgarten

Habeck

2014

Micron

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A Bayesian approach to reconstruction and segmentation of tomographic data is outlined and further detailed for the case of absorption tomography. The algorithm allows the quantification of reconstruction errors and segmentation confidence. Calculation results for various experimental settings (number of projections, incident dose, different materials) are shown and discussed.We are pleased to dedicate this paper to the memory of David J. H. Cockayne. HighlightsSimultaneous tomographic reconstruction and segmentation algorithm Fully self-contained Probabilistic error metrics for both reconstructed densities and segmentation on a voxel basis Highlights (for review)Autonomous reconstruction and segmentation of tomographic data We are pleased to dedicate this paper to the memory of David J. H. Cockayne.

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Compressed sensing electron tomography

Cited by 262 publications

References 64 publications

The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections

The properties of SIRT, TVM, and DART for 3D imaging of tubular domains in nanocomposite thin-films and sections

Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials

Autonomous reconstruction and segmentation of tomographic data

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