University of Oulu

Hauptmann, A., & Smyl, D. (2021). Fusing electrical and elasticity imaging. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2200).

Fusing electrical and elasticity imaging

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Author: Hauptmann, Andreas1,2; Smyl, Danny3
Organizations: 1University of Oulu, Research Unit of Mathematical Sciences, Finland
2University College London, Department of Computer Science, UK
3University of Sheffield, Department of Civil and Structural Engineering, UK
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.7 MB)
Persistent link:
Language: English
Published: Royal Society Publishing, 2021
Publish Date: 2021-05-17


Electrical and elasticity imaging are promising modalities for a suite of different applications, including medical tomography, non-destructive testing and structural health monitoring. These emerging modalities are capable of providing remote, non-invasive and low-cost opportunities. Unfortunately, both modalities are severely ill-posed nonlinear inverse problems, susceptive to noise and modelling errors. Nevertheless, the ability to incorporate complimentary datasets obtained simultaneously offers mutually beneficial information. By fusing electrical and elastic modalities as a joint problem, we are afforded the possibility to stabilize the inversion process via the utilization of auxiliary information from both modalities as well as joint structural operators. In this study, we will discuss a possible approach to combine electrical and elasticity imaging in a joint reconstruction problem giving rise to novel multi-modality applications for use in both medical and structural engineering.

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Series: Philosophical transactions of the Royal Society. A, Mathematical, physical and engineering sciences
ISSN: 1364-503X
ISSN-E: 1471-2962
ISSN-L: 1364-503X
Volume: 379
Issue: 2200
Article number: 20200194
DOI: 10.1098/rsta.2020.0194
Type of Publication: A1 Journal article – refereed
Field of Science: 111 Mathematics
114 Physical sciences
214 Mechanical engineering
Funding: This work was partly funded by Academy of Finland Project 336796 (Finnish Centre of Excellence in Inverse Modelling and Imaging, 2018{2025) as well as Project 334817, and the CMIC-EPSRC platform grant (EP/M020533/1).
Academy of Finland Grant Number: 336796
Detailed Information: 336796 (Academy of Finland Funding decision)
334817 (Academy of Finland Funding decision)
Dataset Reference: The forward EIT model used is made available via the EIDORS project ( The QSEI forward model used is available at (
Copyright information: © 2021 The Author(s). Published by the Royal Society. The final authenticated version is available online at