Data-driven Electrical Conductivity Reconstructions via transceive phase and signal magnitude gradient data from the three imaging directions

Motivation: Phase-based conductivity reconstructions suffer from poor structural information and lack of conductivity information from through plane (z-direction) phase variations. Goal(s): To present an end-to-end process that utilizes gradient information from the transceive phase and tissue magnitude in all three directions (in-plane: x/y and through plane: z) to address the issue of boundary artifacts in conductivity reconstructions and lack of conductivity information from the z-direction. Approach: This method was trained on simulated data (SNR=50), and tested both on simulated and measured in-vivo data. Results: This approach reduces boundary errors and shows higher accuracy in conductivity reconstructions compared to conventional methods. Impact: In contrast to existing tissue conductivity reconstruction algorithms that operate under the assumption of negligible through-plane (z) transceive phase contributions, our approach demonstrates enhanced efficacy and more accurate conductivity reconstructions by explicitly considering through-plane phase variations.