Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

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...

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Autores principales:	Saurav Z K Sajib, Munish Chauhan, Oh In Kwon, Rosalind J Sadleir
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d315a2916c2e4751a3f3824725c51d15
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spelling	oai:doaj.org-article:d315a2916c2e4751a3f3824725c51d152021-12-02T20:06:35ZMagnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.1932-620310.1371/journal.pone.0254690https://doaj.org/article/d315a2916c2e4751a3f3824725c51d152021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254690https://doaj.org/toc/1932-6203Diffusion 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.Saurav Z K SajibMunish ChauhanOh In KwonRosalind J SadleirPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254690 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saurav Z K Sajib Munish Chauhan Oh In Kwon Rosalind J Sadleir Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
description	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.
format	article
author	Saurav Z K Sajib Munish Chauhan Oh In Kwon Rosalind J Sadleir
author_facet	Saurav Z K Sajib Munish Chauhan Oh In Kwon Rosalind J Sadleir
author_sort	Saurav Z K Sajib
title	Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
title_short	Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
title_full	Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
title_fullStr	Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
title_full_unstemmed	Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.
title_sort	magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-a machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d315a2916c2e4751a3f3824725c51d15
work_keys_str_mv	AT sauravzksajib magneticresonancebasedmeasurementofelectromagneticfieldsandconductivityinvivousingsinglecurrentadministrationamachinelearningapproach AT munishchauhan magneticresonancebasedmeasurementofelectromagneticfieldsandconductivityinvivousingsinglecurrentadministrationamachinelearningapproach AT ohinkwon magneticresonancebasedmeasurementofelectromagneticfieldsandconductivityinvivousingsinglecurrentadministrationamachinelearningapproach AT rosalindjsadleir magneticresonancebasedmeasurementofelectromagneticfieldsandconductivityinvivousingsinglecurrentadministrationamachinelearningapproach
_version_	1718375388672950272

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

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