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Performance evaluation of current density based magnetic resonance electrical impedance tomography reconstruction algorithms (mr-eit)

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2009
Boyacıoğlu, Rasim
Magnetic Resonance Electrical Impedance Tomography (MREIT) reconstructs conductivity distribution with internal current density (MRCDI) and boundary voltage measurements. There are many algorithms proposed for the solution of MREIT inverse problem which can be divided into two groups: Current density (J) and magnetic flux density (B) based reconstruction algorithms. In this thesis, J-based MREIT reconstruction algorithms are implemented and optimized with modifications. These algorithms are simulated with five conductivity models which have different geometries and conductivity values. Results of simulation are discussed and reconstruction algorithms are compared according to their performances. Equipotential-Projection algorithm has lower error percentages than other algorithms for noise-free case whereas Hybrid algorithm has the best performance for noisy cases. Although J-substitution and Hybrid algorithms have relatively long reconstruction times, they produced the best images perceptually. v Integration along Cartesian Grid Lines and Integration along Equipotential Lines algorithms diverge as noise level increases. Equipotential-Projection algorithm has erroneous lines starting from corners of FOV especially for noisy cases whereas Solution as a Linear Equation System has a typical grid artifact. When performance with data of experiment 1 is considered, only Solution as a Linear Equation System algorithm partially reconstructed all elements which show that it is robust to noise. Equipotential-Projection algorithm reconstructed resistive element partially and other algorithms failed in reconstruction of conductivity distribution. Experimental results obtained with a higher conductivity contrast show that Solution as a Linear Equation System, J-Substitution and Hybrid algorithms reconstructed both phantom elements and Hybrid algorithm is superior to other algorithms in percentage error comparison.