Performance evaluation of magnetic flux density based magnetic resonance electrical impedance tomography reconstruction algorithms

Eker, Gökhan
Magnetic Resonance Electrical Impedance Tomography (MREIT) reconstructs images of electrical conductivity distribution based on magnetic flux density (B) measurements. Magnetic flux density is generated by an externally applied current on the object and measured by a Magnetic Resonance Imaging (MRI) scanner. With the measured data and peripheral voltage measurements, the conductivity distribution of the object can be reconstructed. There are two types of reconstruction algorithms. First type uses current density distributions to reconstruct conductivity distribution. Object must be rotated in MRI scanner to measure three components of magnetic flux density. These types of algorithms are called J-based reconstruction algorithms. The second type of reconstruction algorithms uses only one component of magnetic flux density which is parallel to the main magnetic field of MRI scanner. This eliminates the need of subject rotation. These types of algorithms are called B-based reconstruction algorithms. In this study four of the B-based reconstruction algorithms, proposed by several research groups, are examined. The algorithms are tested by different computer models for noise-free and noisy data. For noise-free data, the algorithms work successfully. System SNR 30, 20 and 13 are used for noisy data. For noisy data the performance of algorithm is not as satisfactory as noise-free data. Twice differentiation of z component of B (Bz) is used for two of the algorithms. These algorithms are very sensitive to noise. One of the algorithms uses only one differentiation of Bz so it is immune to noise. The other algorithm uses sensitivity matrix to reconstruct conductivity distribution.


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Citation Formats
G. Eker, “Performance evaluation of magnetic flux density based magnetic resonance electrical impedance tomography reconstruction algorithms,” M.S. - Master of Science, Middle East Technical University, 2009.