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.

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Presentation transcript:

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

Introduction: EIT of acute stroke Ischaemic Haemorrhagic 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 !

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

Method: Fraction model The following assumptions are made: 1.the domain is composed of a known number T of tissues with distinct conductivity, 2.the conductivity of each tissue is known for all measurement frequencies, 3.the conductivity of the nth element is given by the linear combination of the conductivities of the component tissues where and.

x ω background perturbation x x ω Method: Fraction model ? ConductivityTissue spectraFraction values

Method: Fraction reconstruction Conductivity Fractions Markov Random field regularization:

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

Results: Use of difference data Phantom FractionsAbsolute Conductivities Difference data Absolute data

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

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

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?

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. Future work Hidden variable Tissue properties 1.Reconstruction 2.Classification

Centre for Medical Imaging and Computing (CMIC) Electrical Impedance Tomography (EIT) Research Group Department of Medical Physics and Bioengineering, University College London Thank for your attention