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Inverse problem of EIT using spectral constraints

Inverse problem of EIT using spectral constraints. Emma Malone 1 , Gustavo Santos 1 , David Holder 1 , Simon Arridge 2 1 Department of Medical Physics and Bioengineering, University College London, UK 2 Department of Computer Science, University College London, UK.

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Inverse problem of EIT using spectral constraints

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  1. Inverse problem of EIT using spectral constraints Emma Malone1, Gustavo Santos1, David Holder1, Simon Arridge2 1 Department of Medical Physics and Bioengineering, University College London, UK 2 Department of Computer Science, University College London, UK

  2. Introduction: EIT of acute stroke • Stroke is the leading cause of disability and third cause of mortality in industrialized nations. • Clot-busting drugs can improve the outcome of ischaemic stroke, but they need to be administered FAST! Ischaemic Haemorrhagic

  3. Introduction: Multifrequency EIT Nonlinear absolute High sensitivity to errors Simple FD Very limited application perturbation background Weighted FD Limited application Jun et al (2009), Phys. Meas., 30(10), 1087-99.

  4. Method: Fraction model The following assumptions are made: the domain is composed of a known number T of tissues with distinctconductivity, the conductivity of each tissue is known for all measurement frequencies, the conductivity of the nth element is given by the linear combination of the conductivities of the component tissues where and .

  5. Method: Fraction model perturbation 1 0 x ω x 1 background 0 x ω Conductivity Tissue spectra Fraction values ?

  6. Method: Fraction reconstruction Fractions Conductivity Markov Random field regularization:

  7. Method: Fraction reconstruction Numerical validation Minimize… Model …subject to Fractions Step 1. Gradient projection Step 2. Damped Gauss-Newton repeat

  8. Results: Use of difference data Phantom Difference data Absolute data Fractions Absolute Conductivities

  9. Results: Use of all multifrequency data Phantom All frequencies Single frequency Fractions WFD Conductivities

  10. Results: Use of nonlinear method Model Linear method Nonlinear method Fractions WFD Conductivities

  11. Discussion • Advantages: • Simultaneous and direct use of all multifrequency data • Nonlinear reconstruction method • Use of difference data • Disadvantage: • Requires accurate knowledge of tissue spectra. • Temperature? • Flow rate? • Cell count?

  12. Future work Tissue properties Hidden variable Reconstruction Classification Hiltunen P, Prince S J D, & Arridge S (2009). A combined reconstruction-classification method for diffuse optical tomography. Physics in medicine and biology, 54(21), 6457–76.

  13. Thank for your attention emma.malone.11@ucl.ac.uk Centre for Medical Imaging and Computing (CMIC) Electrical Impedance Tomography (EIT) Research GroupDepartment of Medical Physics and Bioengineering, University College London

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