Sasha Hakhu ¹ Leland S Hu ² Scott Beeman ¹ Rosalind Sadleir ^1*

• ¹ School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States
• ² Mayo Clinic Arizona, Scottsdale, Arizona, United States

Magnetic resonance-based electrical conductivity imaging provides a potential new contrast mechanism that may improve disease diagnosis. The electrodeless low-frequency conductivity imaging technique of conductivity tensor imaging (CTI) incorporates data from MR diffusion microstructure imaging to reconstruct images. However, each microstructure imaging method uses a different diffusion model multiple parameters and different methods predict differing tissue conductivity estimates. In this study we used publicly available diffusion databases gathered from neurotypical adults determine microstructure parameters for three brain models (NODDI, SANDI and Spherical Mean). We demonstrate the range of conductivities predicted in gray and white matter, and the ranges observed in specific examples of gray and white matter. Conductivities predicted by each method varied greatly and different conductivities were predicted by each model for each tissue type. We also found that many white and grey matter tissues had equivalent bilateral conductivities for each microstructure method. None of the predicted conductivities were close to those made by spectroscopic models of tissue conductivity, but those found using the Spherical Mean technique were closer to those found in related experimental studies. We believe that this work provides an important basis for the developing use of microstructure diffusion methods in low-frequency electrical conductivity imaging.