Evaluation of Ultrasonic Waveform for Flaw-Image Reconstruction Using EMAT Probe

2012 ◽  
Vol 721 ◽  
pp. 243-248
Author(s):  
Yoshihiro Nishimura ◽  
Takayuki Suzuki
Keyword(s):  
Image Reconstruction ◽  
Response Function ◽  
Reference Sample ◽  
3D Images ◽  
Flaw Image ◽  
Internal Flaw

Equations for reconstructing 3D images of internal flaws are presented. Waveforms were measured from a simple reference sample to derive the response function of the probe, and the response function was calculated based on those waveforms. A sample with internal flaws was prepared to evaluate the reconstructed images of internal flaws derived by EMAT using magnetostrictives. 3D-images of an internal flaw could be derived using this response function.

Fast 3D image reconstruction algorithm based on artificial intelligence technology

2021 ◽  
pp. 002072092098504
Author(s):  
An Weigang ◽  
Pan Jinxiao
Keyword(s):  
Artificial Intelligence ◽  
High Resolution ◽  
Image Reconstruction ◽  
Three Dimensional ◽  
Feature Analysis ◽  
3D Image ◽  
3D Images ◽  
Dimensional Image ◽  
Fine Grained ◽  
3D Image Reconstruction

In order to improve the 3D reconstruction capability of high-resolution fine-grained 3D images, a fast 3D image reconstruction algorithm based on artificial intelligence technology is proposed. The cross-gradient sharpening detection method is used to collect features and extract information from high-resolution fine-grained three-dimensional images, and establish an edge contour feature detection model for high-resolution fine-grained three-dimensional images. Combining the salient feature analysis method and the subspace feature analysis method to cluster and analyze the high-resolution fine-grained three-dimensional image. In the artificial intelligence environment, the saliency of the three-dimensional image is detected and analyzed, and the multi-dimensional segmentation and gray histogram of the high-resolution fine-grained three-dimensional image are reconstructed through the subspace segmentation method. According to the reconstruction results of the gray histogram, fast 3D image reconstruction and image fusion processing are performed. Finally, the accurate detection and recognition of the reconstructed image is realized. The simulation results show that this method has a good effect on 3D image reconstruction, and the time cost of image reconstruction is relatively short. It improves the recognition and feature analysis capabilities of high-resolution fine-grained 3D images, and has good application value in the reconstruction, detection and recognition of high-resolution fine-grained 3D images.

Fluorescence Image Reconstruction for Optical Tomography Based on Transient Radiation Transfer Equation

2003 ◽  
Author(s):  
Haiyong Quan ◽  
Zhixiong Guo
Keyword(s):  
Laser Radiation ◽  
Image Reconstruction ◽  
Transfer Equation ◽  
Radiation Transfer ◽  
Optical Tomography ◽  
Imaging Method ◽  
Radiation Transfer Equation ◽  
Participating Media ◽  
3D Images ◽  
3D Image Reconstruction

Image reconstruction is a bottleneck problem that impedes real time application of optical tomography technology. In this paper, we propose a novel fluorescence optical tomography method with a fast yet accurate algorithm for 3D image reconstruction. This imaging method is demonstrated using radiation transfer modeling based design. First the transport of ultrafast laser radiation governed by radiation transfer equation in participating media is simulated. Then the transient fluorescence field is obtained by solving the same radiation transfer equation in which the quantum yield of fluorescence is added to correlate the absorbed laser radiation with fluorescence emission intensity. Finally, 3D images are reconstructed using the temporal signals of fluorescence at detectors around the boundary of targeted tissues. We use the early time of fluorescence flight and the maximum fluorescence intensity to directly reconstruct the 3D images. Two new concepts, i.e., the photon migration statistic property and the solid geometric correlation property, are introduced for signal and image processing, respectively. The image reconstruction in this new method is very fast and does not require any inverse optimization. The accurate and efficient image and location of a 2.4×2.4×2.4mm3 tumor embedded at two different locations inside a 20×20×20mm3 rectangular tissue are demonstrated.

scholarly journals Simulation of 3D Image Reconstruction in Rigid body Motion

2018 ◽  
Vol 232 ◽  
pp. 02002
Author(s):  
Huihong Chen ◽  
Shiming Li
Keyword(s):  
Image Reconstruction ◽  
Rigid Body ◽  
Rigid Body Motion ◽  
Corner Detection ◽  
Body Motion ◽  
3D Image ◽  
3D Images ◽  
3D Image Reconstruction ◽  
Contour Feature

3D image reconstruction under rigid body motion is affected by rigid body motion and visual displacement factors, which leads to low quality of 3D image reconstruction and more noise, in order to improve the quality of 3D image reconstruction of rigid body motion. A 3D image reconstruction technique is proposed based on corner detection and edge contour feature extraction in this paper. Region scanning and point scanning are combined to scan rigid body moving object image. The wavelet denoising method is used to reduce the noise of the 3D image. The edge contour feature of the image is extracted. The sparse edge pixel fusion method is used to decompose the feature of the 3D image under the rigid body motion. The irregular triangulation method is used to extract and reconstruct the information features of the rigid body 3D images. The reconstructed feature points are accurately calibrated with the corner detection method to realize the effective reconstruction of the 3D images. The simulation results show that the method has good quality, high SNR of output image and high registration rate of feature points of image reconstruction, and proposed method has good performance of 3D image reconstruction.

Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

1974 ◽  
Vol 32 ◽  
pp. 330-331
Author(s):  
R. A. Crowther
Keyword(s):  
Image Reconstruction ◽  
Finite Number ◽  
Density Distribution ◽  
Electron Micrographs ◽  
Mathematical Problem ◽  
Three Dimensional ◽  
Ideal Case ◽  
Dimensional Image ◽  
General Object ◽  
The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

Measurements in 3D images

1991 ◽  
Vol 49 ◽  
pp. 408-409
Author(s):  
John C. Russ
Keyword(s):  
Three Dimensional ◽  
Image Plane ◽  
X Rays ◽  
Traditional Use ◽  
3D Images ◽  
Confocal Scanning Laser Microscope ◽  
Hard Materials ◽  
Depth Direction ◽  
Confocal Scanning ◽  
Confocal Scanning Laser

Three-dimensional (3D) images consisting of arrays of voxels can now be routinely obtained from several different types of microscopes. These include both the transmission and emission modes of the confocal scanning laser microscope (but not its most common reflection mode), the secondary ion mass spectrometer, and computed tomography using electrons, X-rays or other signals. Compared to the traditional use of serial sectioning (which includes sequential polishing of hard materials), these newer techniques eliminate difficulties of alignment of slices, and maintain uniform resolution in the depth direction. However, the resolution in the z-direction may be different from that within each image plane, which makes the voxels non-cubic and creates some difficulties for subsequent analysis.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow
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