Computational approaches in electrical impedance tomography with applications to head imaging

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School of Science | Doctoral thesis (article-based) | Defence date: 2021-11-12
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Date

2021

Department

Matematiikan ja systeemianalyysin laitos
Department of Mathematics and Systems Analysis

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Mcode

Degree programme

Language

en

Pages

56 + app. 112

Series

Aalto University publication series DOCTORAL DISSERTATIONS, 139/2021

Abstract

This thesis considers computational approaches to address the inverse problem arising from electrical impedance tomography (EIT), where the aim is to reconstruct (useful information about) the conductivity distribution inside a physical body from boundary measurements of current and voltages. The problem is nonlinear and highly ill-posed, and it generally presents several theoretical and numerical challenges. In fact, the search for a solution usually requires either carefully selected regularization techniques or simplifying assumptions on the measurement setting. A particular focus is on applying EIT to stroke detection in medical imaging, where measurement and modelling errors considerably deteriorate the available boundary data. To model these uncertainties, a novel computational three-dimensional head model is introduced and utilized to simulate realistic synthetic electrode measurements. According to the studied application, different models for the forward problem are considered, such as the continuum model, the complete electrode model and its smoothened version. The examined solution strategies correspond to different methodologies, ranging from regularized iterative reconstruction algorithms to machine learning techniques. The performance of these methods is assessed via three-dimensional simulated experiments performed in different settings.

Description

Defence is held on 12.11.2021 13:15 – 17:15 Via remote technology (Zoom), https://aalto.zoom.us/j/64903826530

Supervising professor

Hyvönen, Nuutti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland

Thesis advisor

Hyvönen, Nuutti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland

Keywords

inverse problems, electrical impedance tomography, complete electrode model, computational head model, stroke detection, Bayesian inversion, modeling errors, inclusion detection, neural networks

Other note

Parts

  • [Publication 1]: V. Candiani, A. Hannukainen and N. Hyvönen. Computational framework for applying electrical impedance tomography to head imaging. SIAM Journal on Scientific Computing, 41(5), B1034–B1060, October 2019. Full text on Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202001021195.
    DOI: 10.1137/19M1245098 View at publisher
  • [Publication 2]: V. Candiani, J. Dardé, H. Garde and N. Hyvönen. Monotonicity- based reconstruction of extreme inclusions in electrical impedance tomography. SIAM Journal on Mathematical Analysis, 52(6), 6234– 6259, December 2020. Full text on Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202101251411.
    DOI: 10.1137/19M1299219 View at publisher
  • [Publication 3]: V. Candiani and M. Santacesaria. Neural networks for classification of strokes in electrical impedance tomography on a 3D head model. Mathematics in Engineering, 4(4), 1–22, August 2021. Full text on Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202109089055.
    DOI: 10.3934/mine.2022029 View at publisher
  • [Publication 4]: V. Candiani, N. Hyvönen, J. P. Kaipio and V. Kolehmainen. Approximation error method for imaging the human head by electrical tomography. arXiv:2106.06238, 24 pages, June 2021. https://arxiv.org/abs/2106.06238

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https://urn.fi/URN:ISBN:978-952-64-0545-2

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[diss] Perustieteiden korkeakoulu / SCI