Iterative Reconstruction Methods for Hybrid Inverse Problems in Impedance Tomography

2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Kristoffer Hoffmann ◽  
Kim Knudsen
Keyword(s):  
Inverse Problems ◽  
Iterative Reconstruction ◽  
Impedance Tomography ◽  
Reconstruction Methods ◽  
Hybrid Inverse Problems

scholarly journals Row Action Algebraic Reconstruction Techniques Implementation in Graphic Cards using SCILAB (Benchmark in Medical Imaging, Seismic Imaging and Walnut Imaging)

2020 ◽  
Author(s):  
Agah Drajat Garnadi ◽  
Muhammad Ilyas ◽  
M.T. Julianto ◽  
S. Nurdiati
Keyword(s):  
Medical Imaging ◽  
Inverse Problems ◽  
Iterative Reconstruction ◽  
Seismic Tomography ◽  
Seismic Imaging ◽  
Test Problems ◽  
Reconstruction Methods ◽  
Algebraic Reconstruction Techniques ◽  
Algebraic Iterative Reconstruction ◽  
Action Methods

In this article we present a SCILAB implementation of algebraic iterative reconstruction methods for discretisation of inverse problems in imaging. These so-called row action methods rely on semi-convergence for achieving the necessary regularisation of the problem. We implement this method using SCILAB and provide a few simplified test problems: medical tomography, seismic tomography and walnut tomography.Numerical results show the capability of this method for the original and perturbed right-hand side vector.

scholarly journals Parallelization of Karcsmarz Algorithm using Graphic Cards for Seismic Imaging

2017 ◽  
Author(s):  
Agah D. Garnadi ◽  
Muhammad Ilyas
Keyword(s):  
Inverse Problems ◽  
Iterative Reconstruction ◽  
Seismic Imaging ◽  
Test Problems ◽  
Reconstruction Methods ◽  
Algebraic Iterative Reconstruction ◽  
Action Methods

We present a SCILAB implementation of algebraic iterative reconstruction methods for discretizations of inverse problems in seismic imaging. These so-called row action methods rely on semi-convergence for achieving the necessary regularization of the problem. We parallelize this method using NVIDIA graphics cards and SCILAB toolbox. We provide a few simplified test problems in seismic imaging to test those solver.

scholarly journals Evaluation of Algebraic Iterative Image Reconstruction Methods for Tetrahedron Beam Computed Tomography Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Joshua Kim ◽  
Huaiqun Guan ◽  
David Gersten ◽  
Tiezhi Zhang
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Cone Angle ◽  
Iterative Algorithms ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Large Cone Angle ◽  
Using Data ◽  
Dual Detector

Tetrahedron beam computed tomography (TBCT) performs volumetric imaging using a stack of fan beams generated by a multiple pixel X-ray source. While the TBCT system was designed to overcome the scatter and detector issues faced by cone beam computed tomography (CBCT), it still suffers the same large cone angle artifacts as CBCT due to the use of approximate reconstruction algorithms. It has been shown that iterative reconstruction algorithms are better able to model irregular system geometries and that algebraic iterative algorithms in particular have been able to reduce cone artifacts appearing at large cone angles. In this paper, the SART algorithm is modified for the use with the different TBCT geometries and is tested using both simulated projection data and data acquired using the TBCT benchtop system. The modified SART reconstruction algorithms were able to mitigate the effects of using data generated at large cone angles and were also able to reconstruct CT images without the introduction of artifacts due to either the longitudinal or transverse truncation in the data sets. Algebraic iterative reconstruction can be especially useful for dual-source dual-detector TBCT, wherein the cone angle is the largest in the center of the field of view.

Hierarchical and Multi-resolution Neuron Network Computing for Complex Inverse Problems-An Approach Based on Brain-inspired Computing and Learning Method

2021 ◽  
Vol 01 ◽  
Author(s):  
Mingyong Zhou
Keyword(s):  
Deep Learning ◽  
Inverse Problems ◽  
Radar Imaging ◽  
Network Computing ◽  
Neuron Network ◽  
Impedance Tomography ◽  
General Complex ◽  
Mathematical Algorithms ◽  
Ill Posed ◽  
Regular Operators

Background: Complex inverse problems such as Radar Imaging and CT/EIT imaging are well investigated in mathematical algorithms with various regularization methods. However it is difficult to obtain stable inverse solutions with fast convergence and high accuracy at the same time due to the ill-posed property and non-linear property. Objective: In this paper, we propose a hierarchical and multi-resolution scalable method from both algorithm perspective and hardware perspective to achieve fast and accurate solu-tions for inverse problems by taking radar and EIT imaging as examples. Method: We present an extension of discussion on neuromorphic computing as brain-inspired computing method and the learning/training algorithm to design a series of problem specific AI “brains” (with different memristive values) to solve a general complex ill-posed inverse problems that are traditionally solved by mathematical regular operators. We design a hierarchical and multi-resolution scalable method and an algorithm framework to train AI deep learning neuron network and map into the memristive circuit so that the memristive val-ues are optimally obtained. We propose FPGA as an emulation implementation for neuro-morphic circuit as well. Result: We compared the methodology between our approach and traditional regulariza-tion method. In particular we use Electrical Impedance Tomography (EIT) and Radar imaging as typical examples to compare how to design an AI deep learning neuron network architec-tures to solve inverse problems. Conclusion: With EIT imaging as a typical example, we show that any moderate complex inverse problem, as long as it can be described as combinational problem, AI deep learning neuron network is a practical alternative approach to try to solve the inverse problems with any given expected resolution accuracy, as long as the neuron network width is large enough and computational power is strong enough for all combination samples training purpose.

Applications of Iterative Reconstruction Methods in SPECT

1992 ◽  
pp. 501-521 ◽  
Author(s):  
B. M. W. Tsui ◽  
G. T. Gullberg ◽  
H. B. Hu ◽  
J. G. Ballard ◽  
D. R. Gilland ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Reconstruction Methods

Using Extreme Learning Machines and the Backprojection Algorithm as an Alternative to Reconstruct Electrical Impedance Tomography Images

2021 ◽  
pp. 16-27
Author(s):  
Juliana Carneiro Gomes ◽  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
Ricardo Emmanuel de Souza ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Imaging Techniques ◽  
Low Cost ◽  
Computational Cost ◽  
Extreme Learning Machines ◽  
Impedance Tomography ◽  
Learning Machines ◽  
Electrical Potentials ◽  
Reconstruction Methods

Electrical Impedance Tomography (EIT) is an imaging technique based on the excitation of electrode pairs applied to the surface of the imaged region. The electrical potentials generated from alternating current excitation are measured and then applied to boundary-based reconstruction methods. When compared to other imaging techniques, EIT is considered a low-cost technique without ionizing radiation emission, safer for patients. However, the resolution is still low, depending on efficient reconstruction methods and low computational cost. EIT has the potential to be used as an alternative test for early detection of breast lesions in general. The most accurate reconstruction methods tend to be very costly as they use optimization methods as a support. Backprojection tends to be rapid but more inaccurate. In this work, the authors propose a hybrid method, based on extreme learning machines and backprojection for EIT reconstruction. The results were applied to numerical phantoms and were considered adequate, with potential to be improved using post processing techniques.

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