A Novel Model-Based Conductivity Reconstruction in Magneto-Acousto-Electrical Tomography (MAET): Simulation and Experiment Study

Magneto-acousto-electrical tomography (MAET), which combines ultrasound imaging with electrical impedance tomography (EIT), offers high resolution and high contrast for mapping the internal conductivity distribution of an object. However, it is a challenge to reconstruct the conductivity distribution containing the interior information due to practical system constraints. A significant issue is the limited bandwidth of experimental systems, especially the ultrasound transducer, which can filter out low-frequency components of the measurement signal. Inspired by the multicompartment model paradigm in diffusion MRI, we propose an innovative model-based approach to address this conductivity reconstruction challenge. The object’s conductivity is constrained by the weighted sum of shifted jump models, dramatically reducing the problem’s degrees of freedom. By deriving the measurement signal based on the proposed model, we can extract the conductivity polarity using an overdetermined linear system solved by the least-squares method. Furthermore, by integrating amplitude information from conventional conductivity parameter images with the extracted conductivity polarity, we reconstruct the final relative conductivity distribution. Our methodology has been rigorously tested through numerical simulations with various noise levels (20%, 40%, and 60%), phantom experiments, and pork tissue comparisons against the state-of-the-art method. The results demonstrate that our method offers superior accuracy and stability compared to existing techniques in case of all evaluation metrics root mean square error (RMSE), structure similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). In conclusion, our proposed method is straightforward to implement and applicable in both simulations and practical experiments, providing a potential strategy for conductivity reconstruction in complex objects in the future.