Electromagnetic Modeling and Sensitivity-Based Optimization of Medical Devices
Doctoral thesis, 2013

Electromagnetics is a fundamental part of biomedical engineering and modern healthcare due to the electromagnetic nature of several important processes in the human body and the interactions of electromagnetic fields with the human body. As a consequence, electromagnetics is exploited for diagnostic and therapeutic purposes by a multitude of medical devices. The biomedical engineering society strives to develop and design new methods, as well as, to improve existing methods for diagnosis and therapy. In addition, electromagnetic compatibility of both electromagnetic and non-electromagnetic medical devices must be assessed. These tasks can be complicated since electromagnetic measurements in the human body can be difficult and in some cases harmful to the patient. Furthermore, the human body is highly heterogeneous, which makes predictions of its interaction with electromagnetic fields demanding. In this thesis, these problems are mitigated by means of accurate, unbiased, and automatized electromagnetic modeling that feature a number of disciplines: (i) detailed electromagnetic modeling based on Maxwell’s equations; (ii) mathematics with particular emphasis on numerical analysis and optimization; and (iii) large-scale parallel computations on computer clusters. Progress in these three areas enables larger and more difficult problems to be addressed. In particular, this methodology is applied to three biomedical problems in this thesis. First, the electromagnetics of pacemaker lead heating in MRI is modeled with emphasis on the multi-scale characteristic of the problem. The results show the resonant nature of the problem and that detailed modeling is essential to accurately describe this phenomenon. Second, a method for optimization of sensor positions in magnetic tracking systems is proposed. The method uses powerful mathematics to alleviate the difficulties and computational burden associated with experimental or computational trial-and-error procedures. Third, the estimation procedure in EEG-based source localization is facilitated by exploiting electromagnetic reciprocity during the modeling. This reduces the demands for tailored estimation procedures and removes one obstacle for real-time source localization.

helical conductors

optimal measurements

thin-wire approximation

inverse problems

Convex optimization


design of experiments

MR safety


magnetic resonance imaging

magnetic tracking

optimal sensor placement

HB3, Hörsalsvägen 10, Chalmers University of Technology, Göteborg
Opponent: Prof. Richard Bayford, School of Science and Technology, Middlesex University, London, UK.


Oskar Talcoth

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Monolithic Multi-Scale Modeling of MR-Induced Pacemaker Lead Heating

13th International Conference on Electromagnetics in Advanced Applications (ICEAA), 2011, Torino, 12-16 September 2011,; (2011)p. 599 - 602

Paper in proceeding

Sensor selection in magnetic tracking based on convex optimisation

Electronics Letters,; Vol. 49(2013)p. 15-16

Journal article

Parameter Scaling in Non-Linear Microwave Tomography

2012 Loughborough Antennas & Propagation Conference, Loughborough, United Kingdom, 12-13 November 2012,; (2012)

Paper in proceeding


C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering



HB3, Hörsalsvägen 10, Chalmers University of Technology, Göteborg

Opponent: Prof. Richard Bayford, School of Science and Technology, Middlesex University, London, UK.

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