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Optimum Current Injection Strategy For Magnetic Resonance Electrical Impedance Tomography

In this thesis, optimum current injection strategy for Magnetic Resonance Electrical
Impedance Tomography (MREIT) is studied. Distinguishability measure based on
magnetic flux density is defined for MREIT. Limit of distinguishability is
analytically derived for an infinitely long cylinder with concentric and eccentric
inhomogeneities. When distinguishability limits of MREIT and Electrical
Impedance Tomography (EIT) are compared, it is found that MREIT is capable of
detecting smaller perturbations than EIT. When conductivities of inhomogeneity
and background object are equal to 0.8S and 1S respectively, MREIT provides
improvement of %74 in detection capacity. Optimum current injection pattern is
found based on the distinguishability definition. For 2-D cylindrical body with
concentric and eccentric inhomogeneities, opposite drive provides best result. As
for the 3-D case, a sphere with azimuthal symmetry is considered.
Distinguishability limit expression is obtained and optimum current injection
pattern is again opposite drive. Based these results, optimum current injection
principles are provided and Regional Image Reconstruction (RIR) using optimum
currents is proposed. It states that conductivity distribution should be reconstructed
for a region rather than for the whole body. Applying current injection principles
and RIR provides reasonable improvement in image quality when there is noise in
the measurement data. For the square geometry, when SNR is 13dB, RIR provides
decrement of nearly %50 in conductivity error rate of small inhomogeneity. Pulse
sequence optimization is done for Gradient Echo (GE) and it is compared with Spin
Echo (SE) in terms of their capabilities for MREIT.

Identiferoai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609340/index.pdf
Date01 February 2008
CreatorsAltunel, Haluk
ContributorsEyuboglu, Haluk
PublisherMETU
Source SetsMiddle East Technical Univ.
LanguageEnglish
Detected LanguageEnglish
TypePh.D. Thesis
Formattext/pdf
RightsTo liberate the content for public access

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