Phase-based conductivity mapping using MRI data contains an assumption of locally constant complex permittivity and use of a differential operator which result in significant inaccuracies at tissue...Show morePhase-based conductivity mapping using MRI data contains an assumption of locally constant complex permittivity and use of a differential operator which result in significant inaccuracies at tissue boundaries and amplification of noise in data. This work focuses on the implementation of an iterative model-based nonlinear optimization algorithm that aims to surpass these rising inaccuracies. The algorithm is designed to optimize conductivity maps using phase data acquired from MRI. In addition to optimization, the algorithm focuses on regularization which further improves the optimized outcome of the conductivity maps. Successful results are demonstrated using both simulated as well as phantom data. The comparison between results of a conventional phase-based conductivity mapping and the iterative algorithm shows improved accuracy for the latter. In addition, the model-based algorithm possesses potential for reduced acquisition time as it is capable of reconstructing accurate conductivity maps with relatively low SNR. In the future, experiments on in-vivo data can be performed. Additionally, to improve the accuracy of the conductivity maps even further, implementation of optimal methods to determine regularization parameters and regularization functions is possible.Show less
