Experimental electrical impedance tomography (II)

The report reviews the essence of Electrical Impedance Tomography (EIT) and studies the different variations of reconstruction algorithms to evaluate their sensitivity and potential for medical imaging. EIT is an interesting method for clinically monitoring patients due to its non-invasive methodolo...

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Main Author: Lee, Elson Jun Yuan
Other Authors: Ng Boon Poh
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/145379
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author Lee, Elson Jun Yuan
author2 Ng Boon Poh
author_facet Ng Boon Poh
Lee, Elson Jun Yuan
author_sort Lee, Elson Jun Yuan
collection NTU
description The report reviews the essence of Electrical Impedance Tomography (EIT) and studies the different variations of reconstruction algorithms to evaluate their sensitivity and potential for medical imaging. EIT is an interesting method for clinically monitoring patients due to its non-invasive methodology in providing continuous imaging of body part, organ, or object impedance. However, the image reconstruction in EIT is not sequential and an incomparable inverse problem. This is due to the use of Tikhonov prior with L2 regularization, a common way to solve EIT problems which that it always smooths out sharp changes or cease regions of the reconstruction. [1] Hence the use of L1 regularization in image reconstruction enables to direct this difficulty. L1 norm on data terms provide estimation strongly built to outliers. In view of this, L1 norm on regularization terms reconstructs sharp spatial profiles. The report comprises of a comprehensive study made on the performance of two different groups of regularization algorithm namely L1 and L2 regularization when challenged by Gaussian noise. L1 regularization group consists of least absolute shrinkage and selection operator (LASSO), Elastic Net and Total Variation (TV) algorithms. While L2 regularization group consists of Newton's One‐Step Error Reconstructor (NOSER) prior, Tikhonov prior and Laplace prior algorithms. Simulated results of L1 and L2 regularization methods via MATLAB were compared to determine which group of regularization can produced improved quality of image reconstruction yet withstand higher noise levels in measured voltages.
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spelling ntu-10356/1453792023-07-07T18:14:15Z Experimental electrical impedance tomography (II) Lee, Elson Jun Yuan Ng Boon Poh School of Electrical and Electronic Engineering EBPNG@ntu.edu.sg Engineering::Electrical and electronic engineering The report reviews the essence of Electrical Impedance Tomography (EIT) and studies the different variations of reconstruction algorithms to evaluate their sensitivity and potential for medical imaging. EIT is an interesting method for clinically monitoring patients due to its non-invasive methodology in providing continuous imaging of body part, organ, or object impedance. However, the image reconstruction in EIT is not sequential and an incomparable inverse problem. This is due to the use of Tikhonov prior with L2 regularization, a common way to solve EIT problems which that it always smooths out sharp changes or cease regions of the reconstruction. [1] Hence the use of L1 regularization in image reconstruction enables to direct this difficulty. L1 norm on data terms provide estimation strongly built to outliers. In view of this, L1 norm on regularization terms reconstructs sharp spatial profiles. The report comprises of a comprehensive study made on the performance of two different groups of regularization algorithm namely L1 and L2 regularization when challenged by Gaussian noise. L1 regularization group consists of least absolute shrinkage and selection operator (LASSO), Elastic Net and Total Variation (TV) algorithms. While L2 regularization group consists of Newton's One‐Step Error Reconstructor (NOSER) prior, Tikhonov prior and Laplace prior algorithms. Simulated results of L1 and L2 regularization methods via MATLAB were compared to determine which group of regularization can produced improved quality of image reconstruction yet withstand higher noise levels in measured voltages. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-12-20T23:36:08Z 2020-12-20T23:36:08Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/145379 en A3340-192 application/pdf Nanyang Technological University
spellingShingle Engineering::Electrical and electronic engineering
Lee, Elson Jun Yuan
Experimental electrical impedance tomography (II)
title Experimental electrical impedance tomography (II)
title_full Experimental electrical impedance tomography (II)
title_fullStr Experimental electrical impedance tomography (II)
title_full_unstemmed Experimental electrical impedance tomography (II)
title_short Experimental electrical impedance tomography (II)
title_sort experimental electrical impedance tomography ii
topic Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/145379
work_keys_str_mv AT leeelsonjunyuan experimentalelectricalimpedancetomographyii