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Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study

Nele De Geeter (UGent) , Guillaume Crevecoeur (UGent) and Luc Dupré (UGent)
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Abstract
Accurate estimation of the human head conductivity is important for the diagnosis and therapy of brain diseases. Induced Current - Magnetic Resonance Electrical Impedance Tomography (IC-MREIT) is a recently developed non-invasive technique for conductivity estimation. This paper presents a formulation where a low number of material parameters need to be estimated, starting from MR eddy-current field maps. We use a parameterized frequency dependent 4-Cole-Cole material model, an efficient independent impedance method for eddy-current calculations and a priori information through the use of voxel models. The proposed procedure circumvents the ill-posedness of traditional IC-MREIT and computational efficiency is obtained by using an efficient forward eddy-current solver.
Keywords
IC-MREIT, conductivity, 4-Cole-Cole

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Citation

Please use this url to cite or link to this publication:

Chicago
De Geeter, Nele, Guillaume Crevecoeur, and Luc Dupré. 2010. “Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for Quantitative Conductivity Estimation of Brain Tissues Using a Priori Information: a Simulation Study.” In IEEE Engineering in Medicine and Biology Society Conference Proceedings, 5669–5672. New York, NY, USA: IEEE.
APA
De Geeter, N., Crevecoeur, G., & Dupré, L. (2010). Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study. IEEE Engineering in Medicine and Biology Society Conference Proceedings (pp. 5669–5672). Presented at the 2010 32nd Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010) : Merging medical humanism and technology, New York, NY, USA: IEEE.
Vancouver
1.
De Geeter N, Crevecoeur G, Dupré L. Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study. IEEE Engineering in Medicine and Biology Society Conference Proceedings. New York, NY, USA: IEEE; 2010. p. 5669–72.
MLA
De Geeter, Nele, Guillaume Crevecoeur, and Luc Dupré. “Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for Quantitative Conductivity Estimation of Brain Tissues Using a Priori Information: a Simulation Study.” IEEE Engineering in Medicine and Biology Society Conference Proceedings. New York, NY, USA: IEEE, 2010. 5669–5672. Print.
@inproceedings{1066038,
  abstract     = {Accurate estimation of the human head conductivity is important for the diagnosis and therapy of brain diseases. Induced Current - Magnetic Resonance Electrical Impedance Tomography (IC-MREIT) is a recently developed non-invasive technique for conductivity estimation. This paper presents a formulation where a low number of material parameters need to be estimated, starting from MR eddy-current field maps. We use a parameterized frequency dependent 4-Cole-Cole material model, an efficient independent impedance method for eddy-current calculations and a priori information through the use of voxel models. The proposed procedure circumvents the ill-posedness of traditional IC-MREIT and computational efficiency is obtained by using an efficient forward eddy-current solver.},
  author       = {De Geeter, Nele and Crevecoeur, Guillaume and Dupr{\'e}, Luc},
  booktitle    = {IEEE Engineering in Medicine and Biology Society Conference Proceedings},
  isbn         = {9781424441242},
  issn         = {1557-170X},
  language     = {eng},
  location     = {Buenos Aires, Argentina},
  pages        = {5669--5672},
  publisher    = {IEEE},
  title        = {Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study},
  url          = {http://dx.doi.org/10.1109/IEMBS.2010.5627896},
  year         = {2010},
}

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