A Regularized Model-Based Approach to Phase-Based Conductivity Mapping

Phase-based conductivity mapping makes use of a noise amplifying operation and often relies on spatial filtering to reduce noise. This work describes a regularized, model-based approach to conductivity mapping, which is more robust in the presence of noisy phase maps and provides better reconstruction near boundaries. We demonstrate the efficacy of the algorithm in simulations as well as in the human brain at 3.0T. This method provides higher SNR and lower RMSE for reconstructed conductivity maps as compared to a basic spatial filtering approach.

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