Advanced reconstruction algorithms for electron tomography: From comparison to combination

Goris, Bart; Roelandts, Tom; Batenburg, Kees Joost; Mezerji, Hamed Heidari; Bals, Sara

doi:10.1016/j.ultramic.2012.07.003

Cited by 82 publications

(70 citation statements)

References 24 publications

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“…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%

“…Although the SIRT, TVM and DART reconstruction methods have been compared before in the literature [22,23,32,33], these studies are generally based on experimental data, lacking a detailed knowledge of the ground truth. In this work, we have simulated the electron tomography workflow based on a welldefined model of tubular domains in a matrix with slab geometry.…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, TVM in the used implementation is an iterative method based on compressive sensing [23,33]. It assumes that the specimen has only a few components and that the gradient of the specimen is sparse.…”

Section: Tvmmentioning

confidence: 99%

“…The performance of above advanced reconstruction algorithms have been mainly studied based on experimental data [22,33] which generally lack the ground truth of the specimens. 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].…”

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: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Tvmmentioning

confidence: 99%

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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“…U zavisnosti od situacije u kojoj ih treba prepoznati segmenti su različito definisani te ih možemo tretirati na različite načine koji su proučavani u literaturi [26][27][28][29].…”

Section: Segmentacijaunclassified

Quantifying the shape of nanoparticles: Segmentation and elongation measure

Kopanja¹,

Ivković²,

Lončar³

et al. 2017

Zas Mat

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Kvantifikovanje oblika nanočestica: segmentacija i mera izduženosti IZVOD UVODZbog velikih mogućnosti za primenu proučava-nje nanomaterijala zauzima veoma važno mesto u nauci o materijalima. Nanomaterijali su sistemi u kojima su veličine čestica manje od 100 nm. Smanjenjem veličine magnetnih čestica dolazi do promene magnetnih svojstava, tako da magnetni nanomaterijali pokazuju drugačija fizička svojstva: velika koercitivnost, superparamagnetizam, velika magnetna otpornost, niska ili visoka magnetizacija, smanjenje Kirijeve/Nelove temperature. Jedna od veoma važnih anizotropija nanočestica koja određuje magnetna svojstva nanočestičnog materijala je anizotropija oblika, koja je u direktnoj vezi sa oblikom nanočestica. Kod istih materijala vrednosti magnetnih parametara se mogu razlikovati i po desetak puta u zavisnosti od oblika nanočestica [1,2]. Uticaj oblika nanočestica na makroskopska magnetna svojstva nanomaterijala sve više se proučava u literaturi [3,4]. Kao jedno od važnih svojstava oblika izduženost oblika ima bitnu ulogu u razumevanju magnetnih svojstava nanočestičnih materijala (npr. poznato je da se koercitativnost *Autor za korespondenciju: Lazar Kopanja E-mail: kopanjal@yahoo.com Rad primljen: 29. 06. 2017. Rad prihvaćen: 30. 07. 2017 Rad je dostupan na sajtu: www.idk.org.rs/casopis povećava s povećanjem izduženosti). Stoga je pored merenja fizičkih svojstava veoma važno imati način za merenje oblika, odnosno dodeljivanje obliku kvantitativnih vrednosti na osnovu kojih se oni mogu porediti i klasifikovati.Polazna tačka za merenje oblika su mikroskopske slike nanočestičnih materijala koje se zatim analiziraju u cilju utvrđivanja kvantitativnih karakteristika nanočestica i njihovih međusobnih relacija. U postupcima analize slike segmentacija je jedan od prvih i najvažnijih koraka [5][6][7][8]. Kao deo naučne oblasti digitalne slike, koja zauzima sve veću primenu u različitim poljima istraživanja, segmentacija se ne može posmatrati bez uzimanja u obzir velikog boja parametara slike. Naime, za različite potrebe različito se definišu i segmenti, naravno, sve zavisno od situacije u kojoj se slika analizira. Nekada je bitno da se izdvoje samo svetliji delovi slike, pa je onda osnov za segmentaciju luminenca piksela. U drugom slučaju je bitno da se alaliziraju segmenti sa određenim koncentracijama boja, pa su tada RGB komponente osnov za segmentaciju [9]. Ovih primera ima mnogo, a svi oni se svrstavaju u prvi uslov za segmentaciju -zajednička karakteristika pojedinačnih segmenata. Drugi uslov za segmentaciju se definiše u neprekidnosti piksela koji kao prvi detektovani čini jedan segment. Odnosno, segment slike definiše grupa piksela sa

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Three‐dimensional Microstructural Characterization of Polymer Nanocomposites by Electron Tomography

Dalmas

Roiban

2016

Functional and Physical Properties of Polymer Nanocomposites

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Advanced reconstruction algorithms for electron tomography: From comparison to combination

Cited by 82 publications

References 24 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

Quantifying the shape of nanoparticles: Segmentation and elongation measure

Three‐dimensional Microstructural Characterization of Polymer Nanocomposites by Electron Tomography

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