An accelerated threshold-based back-projection algorithm for Compton camera image reconstruction

2010 ◽  
Vol 38 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Daniel W. Mundy ◽  
Michael G. Herman
Keyword(s):  
Image Reconstruction ◽  
Projection Algorithm ◽  
Back Projection ◽  
Compton Camera ◽  
Camera Image

scholarly journals Electrical impedance tomography methods for miniaturised 3D systems

2019 ◽  
Vol 7 (1) ◽  
pp. 59-67 ◽  
Author(s):  
C. Canali ◽  
K. Aristovich ◽  
L. Ceccarelli ◽  
L.B Larsen ◽  
Ø. G. Martinsen ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Projection Algorithm ◽  
Back Projection ◽  
Engineering Applications ◽  
Impedance Tomography ◽  
Invasive Approach ◽  
Test Objects

Abstract In this study, we explore the potential of electrical impedance tomography (EIT) for miniaturised 3D samples to provide a non-invasive approach for future applications in tissue engineering and 3D cell culturing. We evaluated two different electrode configurations using an array of nine circular chambers (Ø 10 mm), each having eight gold plated needle electrodes vertically integrated along the chamber perimeter. As first method, the adjacent electrode configuration was tested solving the computationally simple back-projection algorithm using Comsol Multiphysics in time-difference EIT (t-EIT). Subsequently, a more elaborate method based on the “polar-offset” configuration (having an additional electrode at the centre of the chamber) was evaluated using linear t-EIT and linear weighted frequency-difference EIT (f-EIT). Image reconstruction was done using a customised algorithm that has been previously validated for EIT imaging of neural activity. All the finite element simulations and impedance measurements on test objects leading to image reconstruction utilised an electrolyte having an ionic strength close to physiological solutions. The chosen number of electrodes and consequently number of electrode configurations aimed at maximising the quality of image reconstruction while minimising the number of required measurements. This is significant when designing a technique suitable for tissue engineering applications where time-based monitoring of cellular behaviour in 3D scaffolds is of interest. The performed tests indicated that the method based on the adjacent configuration in combination with the back-projection algorithm was only able to provide image reconstruction when using a test object having a higher conductivity than the background electrolyte. Due to limitations in the mesh quality, the reconstructed image had significant irregularities and the position was slightly shifted toward the perimeter of the chamber. On the other hand, the method based on the polar-offset configuration combined with the customised algorithm proved to be suitable for image reconstruction when using non-conductive and cell-based test objects (down to 1% of the measurement chamber volume), indicating its suitability for future tissue engineering applications with polymeric scaffolds.

Simulative Study of Two-Phase Homogenous and Isotropic Media Imaging using Magnetic Induction Tomography

2014 ◽  
Vol 69 (8) ◽  
Author(s):  
Zulkarnay Zakaria ◽  
Ibrahim Balkhis ◽  
Lee Pick Yern ◽  
Nor Muzakkir Nor Ayob ◽  
Mohd Hafiz Fazalul Rahiman ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Magnetic Induction ◽  
Optical Tomography ◽  
Reconstruction Algorithm ◽  
Projection Algorithm ◽  
Back Projection ◽  
Two Phase ◽  
Excitation Coil ◽  
Magnetic Induction Tomography ◽  
Linear Back Projection

Magnetic induction tomography is a new non-invasive technology, based on eddy current discovery of electromagnetic induction by Michael Faraday. Through this technique, the passive electrical properties distribution of an object can be obtained by the use of image reconstruction algorithm implemented in this system. There are many types of image reconstruction that have been developed for this modality, however in this paper only two algorithms discussed, Linear Back Projection and Eminent Pixel Reconstruction. Linear Back Projection algorithm is the most basic type of image reconstruction. It is the simplest and fast algorithm out of all types of algorithms, whereas Eminent Pixel Reconstruction algorithm is an improved algorithm which provided better images and has been implemented in other modalities such as optical tomography. This paper has implemented Eminent Pixel Reconstruction in magnetic induction tomography applications and the performance is compared to Linear Back Projection based on the simulation of the fourteen types of simulated phantoms of homogenous and isotropic conductivity property. It was found that Eminent Pixel Reconstruction has produced better images relative to Linear Back Projection, however the images are still poor when the objects are located near to the excitation coil or sensor and it is worse when the distance between objects are near to each other.

A Filtered Back-Projection Algorithm for 4π Compton Camera Data

2015 ◽  
Vol 62 (4) ◽  
pp. 1911-1917 ◽  
Author(s):  
Andrew Haefner ◽  
Donald Gunter ◽  
Ross Barnowski ◽  
Kai Vetter
Keyword(s):  
Projection Algorithm ◽  
Back Projection ◽  
Compton Camera ◽  
Filtered Back Projection

scholarly journals Accurate Sheet Resistivity Measurement Based on Image Reconstruction Using Improved Node-back-projection Algorithm

2019 ◽  
Vol 31 (12) ◽  
pp. 4013
Author(s):  
Xinfu Liu ◽  
Qianwen Li ◽  
Hsiung-Cheng Lin ◽  
Pengfei Wu ◽  
Mengdan Wang
Keyword(s):  
Image Reconstruction ◽  
Resistivity Measurement ◽  
Projection Algorithm ◽  
Back Projection ◽  
Sheet Resistivity

An Improved Back-Projection Algorithm for Magnetic Induction Tomography Image Reconstruction

2013 ◽  
Vol 647 ◽  
pp. 630-635 ◽  
Author(s):  
Li Ke ◽  
Xiao Lin ◽  
Qiang Du
Keyword(s):  
Magnetic Field ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Projection Algorithm ◽  
Projection Area ◽  
Homogeneous Field ◽  
Back Projection ◽  
Field Lines ◽  
Magnetic Induction Tomography ◽  
The Magnetic Field

Magnetic induction tomography (MIT) acted as a contactless and non-invasive medical imaging technology has aroused wide concern, while a large amount of calculation and a series of convergence problems in the solution of the inverse problem become technical difficulties for MIT. In order to solve these problems, an improved back-projection image reconstruction algorithm based on the magnetic field lines distribution is presented in this paper. Firstly, the eddy current problem of MIT was solved by the finite element method to obtain the magnetic field distribution. Secondly, the back-projection areas were divided according to the magnetic field lines distribution in the homogeneous field. Finally, image reconstruction was realized by projecting the phase shifts back along the corresponding projection area. The reconstruction results for perturbations with different conductivities appearing at different locations reveal that the improved back-projection algorithm for MIT owning the character of high speed performs well in reflecting location and shape information of the perturbation.

PEMBELAJARAN SIMULASI PENCITRAAN CT DENGAN MENGGUNAKAN PRINSIP REKONSTRUKSI CITRA DALAM SOFTWARE MATLAB

2015 ◽  
Vol 8 (3) ◽  
pp. 161
Author(s):  
Samuel Gideon
Keyword(s):  
Image Reconstruction ◽  
Ct Imaging ◽  
The Body ◽  
Reconstruction Technique ◽  
Imaging Features ◽  
Back Projection ◽  
Cross Sectional ◽  
Low Contrast ◽  
Image Reconstruction Technique ◽  
Imaging Algorithms

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab

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