Reconstruction Algorithm of Cone-Beam Phase X-Ray Computer-Tomography Based on Grating Imaging

2008 ◽  
Vol 28 (6) ◽  
pp. 1079-1084
Author(s):  
陈欣 Chen Xin ◽  
孙怡 Sun Yi ◽  
朱佩平 Zhu Peiping
Keyword(s):  
Computer Tomography ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
X Ray

Reconstruction Algorithm of Fan-Beam Helical X-Ray Computer Tomography Based on Grating Imaging

2010 ◽  
Vol 30 (2) ◽  
pp. 421-427 ◽  
Author(s):  
李镜 Li Jing ◽  
刘文杰 Liu Wenjie ◽  
朱佩平 Zhu Peiping ◽  
孙怡 Sun Yi
Keyword(s):  
Computer Tomography ◽  
Reconstruction Algorithm ◽  
X Ray

Near Real-Time X-Ray Cone-Beam Microtomography

1999 ◽  
Vol 5 (S2) ◽  
pp. 940-941
Author(s):  
Shih Ang ◽  
Wang Ge ◽  
Cheng Ping-Chin
Keyword(s):  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Reconstruction Process ◽  
X Ray ◽  
Beam Geometry ◽  
Cone Beam Reconstruction ◽  
Cylindrical Coordinate ◽  
Contrast Mechanism ◽  
Penetration Ability ◽  
Set Up

Due to the penetration ability and absorption contrast mechanism, cone-beam X-ray microtomography is a powerful tool in studying 3D microstructures in opaque specimens. In contrast to the conventional parallel and fan-beam geometry, the cone-beam tomography set up is highly desirable for faster data acquisition, build-in magnification, better radiation utilization and easier hardware implementation. However, the major draw back of the cone-beam reconstruction is its computational complexity. In an effort to maximize the reconstruction speed, we have developed a generalized Feldkamp cone-beam reconstruction algorithm to optimize the reconstruction process. We report here the use of curved voxels in a cylindrical coordinate system and mapping tables to further improve the reconstruction efficiency.The generalized Feldkamp cone-beam image reconstruction algorithm is reformulated utilizing mapping table in the discrete domain as: , where .

Reconstruction Algorithm for Cone-Beam X-Ray CT Using Inverse Filtering

1990 ◽  
Vol 21 (3) ◽  
pp. 25-34
Author(s):  
Hiroshi Matsuo ◽  
Aklra Iwata ◽  
Nobuo Suzumura ◽  
Isao Horiba
Keyword(s):  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Inverse Filtering ◽  
X Ray

X-ray Nano- and Micro-tomography in an SEM

2013 ◽  
Vol 21 (2) ◽  
pp. 24-28 ◽  
Author(s):  
Bart Pauwels ◽  
Alexander Sasov
Keyword(s):  
Computer Tomography ◽  
Small Angle ◽  
Reconstruction Algorithm ◽  
X Ray ◽  
Projection Images ◽  
Micro Tomography ◽  
Ray Projection ◽  
Set Up ◽  
Rotation Stage ◽  
Non Destructive

X-ray microfocus computer tomography (μ-CT) is a non-destructive experimental technique that reveals the 3D internal microstructure of the sample under study. The experimental set-up consists of an X-ray source, an X-ray detector, and set in between is a sample that is placed on a rotation stage. With this set-up multiple X-ray projection images can be obtained from the sample at different angles. In between the acquisition of two successive images, the sample is rotated over a small angle, typically between 0.2° and 1°. This set of projection images is then used as input for the reconstruction algorithm, which calculates a reconstruction of the internal microstructure of the sample with (sub-) micrometer sensitivity.

scholarly journals Cone Beam X-Ray Luminescence Tomography Imaging Based on KA-FEM Method for Small Animals

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Dongmei Chen ◽  
Fanzhen Meng ◽  
Fengjun Zhao ◽  
Cao Xu
Keyword(s):  
Small Animal ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Small Animals ◽  
Reconstruction Problem ◽  
X Ray ◽  
Tomography Imaging ◽  
Fem Method ◽  
Ill Posed

Cone beam X-ray luminescence tomography can realize fast X-ray luminescence tomography imaging with relatively low scanning time compared with narrow beam X-ray luminescence tomography. However, cone beam X-ray luminescence tomography suffers from an ill-posed reconstruction problem. First, the feasibility of experiments with different penetration and multispectra in small animal has been tested using nanophosphor material. Then, the hybrid reconstruction algorithm with KA-FEM method has been applied in cone beam X-ray luminescence tomography for small animals to overcome the ill-posed reconstruction problem, whose advantage and property have been demonstrated in fluorescence tomography imaging. The in vivo mouse experiment proved the feasibility of the proposed method.

An accelerated RAMLA reconstruction algorithm for X-ray cone-beam CT

2013 ◽  
Vol 55 (5) ◽  
pp. 237-242
Author(s):  
Xing Zhao ◽  
Jing-Jing Hu ◽  
Tao Yang ◽  
Feng Wang
Keyword(s):  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
X Ray

Cone-beam 3D image reconstruction in x-ray microtomography

1993 ◽  
Vol 51 ◽  
pp. 654-655
Author(s):  
G. Wang ◽  
P. C. Cheng ◽  
T. H. Lin ◽  
D. M. Shinozaki ◽  
H. Kim
Keyword(s):  
Image Reconstruction ◽  
Point Source ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
X Rays ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
3D Image Reconstruction ◽  
Cone Beam Reconstruction ◽  
Mechanical Stage

An X-ray shadow projection microscope system using a scannable point source of X-rays is under development at AMIL-ARTS, SUNY at Buffalo, USA. The point source is generated by a focussed electron beam, which can be steered electromagnetically in a plane perpendicular to the optical axis of the microscope. A specimen is mounted on a rotatable mechanical stage for microtomography. Considering the hardware characteristics of this system and the limitations of current cone-beam reconstruction algorithms, a generalized Feldkamp’s cone-beam image reconstruction algorithm has been developed at our laboratories. In our cone-beam reconstruction, there are mainly two kinds of scanning scanning modes: planar and helix-like. A planar scanning locus is used to handle spherical or plate-like specimens. A typical case of planar scanning loci is a circle, which is used in Feldkamp’s cone-beam reconstruction. A helix-like scanning locus is used to deal with rod-shaped specimens. Without loss of generality, a locus turn of the X-ray source can be defined in cylindrical coordinates by the following equation:

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

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