Assessment of tomographic reconstruction performance using the Mojette transform

2016 ◽  
Author(s):  
Henri Der Sarkissian ◽  
Jeanpierre Guédon ◽  
Benoit Recur ◽  
Nicolas Normand
Keyword(s):  
Tomographic Reconstruction ◽  
Reconstruction Performance ◽  
Mojette Transform

Three-dimensional tomographically reconstructed optical emission profiles of Hall thruster plasmas

2021 ◽  
Author(s):  
jinwoo kim ◽  
Dongho Lee ◽  
Guentae Doh ◽  
Sanghoo Park ◽  
Holak Kim ◽  
...  
Keyword(s):  
Radiative Decay ◽  
Tomographic Reconstruction ◽  
Gaussian White Noise ◽  
Three Dimensional ◽  
Diagnostic System ◽  
Optical Emission ◽  
Ccd Camera ◽  
Hall Thruster ◽  
Plume Region ◽  
Reconstruction Performance

Abstract A diagnostic system was developed for spectrally resolved, three-dimensional tomographic reconstruction of Hall thruster plasmas, and local intensity profiles of Xe I and Xe II emissions were reconstructed. In this diagnostic system, 28 virtual cameras were generated using a single, fixed charge-coupled device (CCD) camera by rotating the Hall thruster to form a sufficient number of lines of sight. The Phillips-Tikhonov regularization algorithm was used to reconstruct local emission profiles from the line-integrated emission signals. The reconstruction performance was evaluated using both azimuthally symmetric and asymmetric synthetic phantom images including 5% Gaussian white noise, which resulted in a root-mean-square error of the reconstruction within an order of 10-3 even for a 1% difference in the azimuthal intensity distribution. Using the developed system, three-dimensional local profiles of Xe II emission (541.9 nm) from radiative decay of the excited state 5p4(3P2)6p2[3]˚5/2 and Xe I emission (881.9 nm) from 5p5(2P˚3/2)6p2[5/2]3 were obtained, and two different shapes were found depending on the wavelength and the distance from the thruster exit plane. In particular, a stretched central jet structure was distinctively observed in the Xe II emission profile beyond 10 mm from the thruster exit, while gradual broadening was found in the Xe I emission. Approximately 10% azimuthal nonuniformities were observed in the local Xe I and Xe II intensity profiles in the near-plume region (< 10 mm), which could not be quantitatively distinguished by analysis of the frontal photographic image. Three-dimensional Xe I and Xe II intensity profiles were also obtained in the plume region, and the differences in the structures of both emissions were visually confirmed.

Mojette Transform Tomographic Reconstruction Algorithm Based on Algebraic Iteration

2019 ◽  
Vol 39 (2) ◽  
pp. 0211003
Author(s):  
王佳 Wang Jia ◽  
张玉虹 Zhang Yuhong ◽  
张维光 Zhang Weiguang
Keyword(s):  
Tomographic Reconstruction ◽  
Reconstruction Algorithm ◽  
Mojette Transform

Design and calibration of an IVEM for general 3-dimensional imaging of non-diffracting materials

1992 ◽  
Vol 50 (2) ◽  
pp. 1040-1041
Author(s):  
Neil Rowlands ◽  
Jeff Price ◽  
Michael Kersker ◽  
Seichi Suzuki ◽  
Steve Young ◽  
...  
Keyword(s):  
3D Imaging ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3 Dimensional ◽  
3D Microstructure ◽  
Image Translation ◽  
Digital Correlation ◽  
On Line ◽  
Mechanical Stage ◽  
Projection Angle

Three-dimensional (3D) microstructure visualization on the electron microscope requires that the sample be tilted to different positions to collect a series of projections. This tilting should be performed rapidly for on-line stereo viewing and precisely for off-line tomographic reconstruction. Usually a projection series is collected using mechanical stage tilt alone. The stereo pairs must be viewed off-line and the 60 to 120 tomographic projections must be aligned with fiduciary markers or digital correlation methods. The delay in viewing stereo pairs and the alignment problems in tomographic reconstruction could be eliminated or improved by tilting the beam if such tilt could be accomplished without image translation.A microscope capable of beam tilt with simultaneous image shift to eliminate tilt-induced translation has been investigated for 3D imaging of thick (1 μm) biologic specimens. By tilting the beam above and through the specimen and bringing it back below the specimen, a brightfield image with a projection angle corresponding to the beam tilt angle can be recorded (Fig. 1a).

Methods for three-dimensional reconstruction of cellular components within a thick section

1986 ◽  
Vol 44 ◽  
pp. 18-21
Author(s):  
J. Frank ◽  
B. F. McEwen ◽  
M. Radermacher ◽  
C. L. Rieder
Keyword(s):  
Tilt Angle ◽  
Tomographic Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Thick Section ◽  
Three Dimensional Reconstruction ◽  
Axis Ratio ◽  
Dimensional Reconstruction ◽  
Cellular Components

The tomographic reconstruction from multiple projections of cellular components, within a thick section, offers a way of visualizing and quantifying their three-dimensional (3D) structure. However, asymmetric objects require as many views from the widest tilt range as possible; otherwise the reconstruction may be uninterpretable. Even if not for geometric obstructions, the increasing pathway of electrons, as the tilt angle is increased, poses the ultimate upper limitation to the projection range. With the maximum tilt angle being fixed, the only way to improve the faithfulness of the reconstruction is by changing the mode of the tilting from single-axis to conical; a point within the object projected with a tilt angle of 60° and a full 360° azimuthal range is then reconstructed as a slightly elliptic (axis ratio 1.2 : 1) sphere.

Practical electron tomography

1995 ◽  
Vol 53 ◽  
pp. 742-743
Author(s):  
C.L. Woodcock
Keyword(s):  
Electron Tomography ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3D Shape ◽  
Computational Resources ◽  
Shape Of Particles

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.

Laser tomographic reconstruction in a complex concentration flow field

1999 ◽  
Author(s):  
Jisoo Ha ◽  
Michael Feng ◽  
Frederick Gouldin
Keyword(s):  
Flow Field ◽  
Tomographic Reconstruction

X-Ray Nanotomography (XRMT) Tool for Non-Destructive High-Resolution Imaging of ICs

2001 ◽  
Author(s):  
Wenbing Yun ◽  
Steve Wang ◽  
David Scott ◽  
Kenneth W. Nill ◽  
Waleed S. Haddad
Keyword(s):  
High Resolution ◽  
Process Development ◽  
3D Visualization ◽  
Tomographic Reconstruction ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
Visualization Software ◽  
Volumetric Reconstruction ◽  
Resolution Imaging ◽  
Non Destructive

Abstract A high-resolution table-sized x-ray nanotomography (XRMT) tool has been constructed that shows the promise of nondestructively imaging the internal structure of a full IC stack with a spatial resolution better than 100 nm. Such a tool can be used to detect, localize, and characterize buried defects in the IC. By collecting a set of X-ray projections through the full IC (which may include tens of micrometers of silicon substrate and several layers of Cu interconnects) and applying tomographic reconstruction algorithms to these projections, a 3D volumetric reconstruction can be obtained, and analyzed for defects using 3D visualization software. XRMT is a powerful technique that will find use in failure analysis and IC process development, and may facilitate or supplant investigations using SEM, TEM, and FIB tools, which generally require destructive sample preparation and a vacuum environment.

scholarly journals Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1521
Author(s):  
Jihoon Lee ◽  
Seungwook Yoon ◽  
Euiseok Hwang
Keyword(s):  
Data Compression ◽  
High Frequency ◽  
Spectral Properties ◽  
Situational Awareness ◽  
Time Windows ◽  
Low Frequency ◽  
Smart Meters ◽  
Compression Performance ◽  
Reconstruction Performance ◽  
Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

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