Tissue resistivity estimation in the presence of positional and geometrical uncertainties

Date

2000-08-01

Author

Baysal, U
Eyüboğlu, Behçet Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

157
views

0
downloads

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Subject Keywords

Tomography

URI

https://hdl.handle.net/11511/32791

Journal

PHYSICS IN MEDICINE AND BIOLOGY

DOI

https://doi.org/10.1088/0031-9155/45/8/322

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

ELECTRICAL-IMPEDANCE TOMOGRAPHY USING INDUCED CURRENTS Gençer, Nevzat Güneri; Kuzuoğlu, Mustafa (1994-06-01) The mathematical basis of a new imaging modality, Induced Current Electrical Impedance Tomography (EIT), is investigated. The ultimate aim of this technique is the reconstruction of conductivity distribution of the human body, from voltage measurements made between electrodes placed on the surface, when currents are induced inside the body by applied time varying magnetic fields. In this study the two-dimensional problem is analyzed. A specific 9-coil system for generating nine different exciting magnetic f...
J-Substitution and Filtered Equipotential-Projection Based Hybrid MREIT Reconstruction Algorithm Boyacioglu, Rasim; Eyüboğlu, Behçet Murat (2009-09-12) Magnetic Resonance Electrical Impedance Tomography (MREIT) reconstructs true conductivity images by using current density distribution and surface potential measurements. In this study, equipotential projection [1] and J-substitution image reconstruction [2] algorithms for MREIT are compared. A novel reconstruction method, J-substitution and filtered equipotential-projection based hybrid reconstruction algorithm, is proposed. In this method, the image reconstructed with equipotential projection algorithm is...
Data acquisition system for Lorentz force electrical impedance tomography using magnetic field measurements Kaboutari, Keivan; Gençer, Nevzat Güneri; Department of Electrical and Electronics Engineering (2017) Lorentz Force Electrical Impedance Tomography (LFEIT) is a novel imaging modality to image electrical conductivity properties of biological tissues. This modality is recently proposed for early stage diagnosis of cancerous tissues. The main aim of this thesis study is to develop a data acquisition system for LFEIT. Design of contactless receiver sensor, static magnetic field generation (0.56 T is generated by permanent neodymium magnets), amplification of received signals and experimental studies using vari...
Magnetic Resonance Electrical Impedance Tomography For Anisotropic Conductivity Imaging Degirmenci, E.; Eyüboğlu, Behçet Murat (2008-11-27) Magnetic Resonance Electrical Impedance Tomography (MREIT) brings high resolution imaging of true conductivity distribution to reality. MREIT images are reconstructed based on measurements of current density distribution and a surface potential value, induced by an externally applied current flow. Since biological tissues may be anisotropic, isotropic conductivity assumption, as it is adopted in most of MREIT reconstruction algorithms, introduces reconstruction inaccuracy. In this study, a novel algorithm i...
Image Reconstruction in Magnetic Resonance Conductivity Tensor Imaging (MRCTI) DEĞİRMENCİ, EVREN; Eyüboğlu, Behçet Murat (2012-03-01) Almost all magnetic resonance electrical impedance tomography (MREIT) reconstruction algorithms proposed to date assume isotropic conductivity in order to simplify the image reconstruction. However, it is well known that most of biological tissues have anisotropic conductivity values. In this study, four novel anisotropic conductivity reconstruction algorithms are proposed to reconstruct high resolution conductivity tensor images. Performances of these four algorithms and a previously proposed algorithm are...

Citation Formats

U. Baysal and B. M. Eyüboğlu, “Tissue resistivity estimation in the presence of positional and geometrical uncertainties,” PHYSICS IN MEDICINE AND BIOLOGY, pp. 2373–2388, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32791.