Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study

Abstract

Objectives. (1) Develop a computational pipeline for three-dimensional
fast neural magnetic detection electrical impedance tomography (MDEIT), (2)
determine whether constant current or constant voltage is preferable for MDEIT,
(3) perform reconstructions of simulated neural activity in a human head
model with realistic noise and compare MDEIT to EIT and (4) perform a
two-dimensional study in a saline tank for MDEIT with optically pumped
magnetometers (OPMs) and compare reconstruction algorithms. Approach.
Forward modelling and image reconstruction were performed with a realistic
model of a human head in three dimensions and at three noise levels for
four perturbations representing neural activity. Images were compared using
the error in the position and size of the reconstructed perturbations. Two-
dimensional MDEIT was performed in a saline tank with a resistive perturbation
and one OPM. Six reconstruction algorithms were compared using the error
in the position and size of the reconstructed perturbations. Main Results.
A computational pipeline was developed in COMSOL Multiphysics, reducing
the Jacobian calculation time from months to days. MDEIT reconstructed
images with a lower reconstruction error than EIT with a mean difference of
7.0%, 5.5% and 11% for three noise cases representing current noise, reduced
current source noise and reduced current source and magnetometer noise. A
rank analysis concluded that the MDEIT Jacobian was less rank-deficient than
the EIT Jacobian. Reconstructions of a phantom in a saline tank had a best
reconstruction error of 13%, achieved using 0th order Tikhonov regularisation
with simulated noise-based correction. Significance. This study demonstrated
that three-dimensional MDEIT for neural imaging is feasible and that MDEIT
reconstructed superior images to EIT, which can be explained by the lesser rank
deficiency of the MDEIT Jacobian. Reconstructions of a perturbation in a saline
tank demonstrated a proof of principle for two-dimensional MDEIT with OPMs
and identified the best reconstruction algorithm.

IoT-Driven Experimental Framework for Advancing Electrical Impedance Tomography

“…EIT promises to open up even more opportunities for data-driven automation and decision-making as it develops inside the IoT ecosystem, revolutionizing a variety of sectors and applications. [15][16][17][18] The continuous development and improvement of systems are integral aspects shaped by advancements in memory, speed, power consumption, and transceiver technology, facilitated through various methodologies. Transceiver technology, conceptualized within the system's framework as the Internet of Things (IoT), 17 defines a transformative approach to connectivity.…”