Abstract
Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.
Highlights
Magnetic resonance based methods have recently been used to image parameters characterizing low-frequency electrical current flow in the human body, including current density and conductivity tensors [1]
Specific techniques used to extract scaling factors from single current applications are described including assumptions and calculations involved in simulating magnetic flux density data and current density to generate training data and methods used to reconstruct quantities such as current density and electric fields
Reconstructed scale factor images solving Eq (9) estimated from measured current density and diffusion tensor data are displayed as Z^v and Z^h in Fig 5(c) and 5(d) for vertical and horizontal currents, respectively
Summary
Magnetic resonance based methods have recently been used to image parameters characterizing low-frequency electrical current flow in the human body, including current density and conductivity tensors [1]. Because only one component of magnetic flux density, Bz, the component along the longitudinal axis of an MRI system, can be measured conveniently, specialized techniques have been developed to recover conductivity information from these data [1]. Machine Learning approach for measurement of electromagnetic fields and low-frequency conductivity in vivo collection and analysis, decision to publish, or preparation of the manuscript
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
