Display options
Cite
Eroğlu HH, Puonti O, Göksu C, et al. On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives. Neuroimage. 2021;243:118517doi: 10.1016/j.neuroimage.2021.118517.
Eroğlu, H. H., Puonti, O., Göksu, C., Gregersen, F., Siebner, H. R., Hanson, L. G., & Thielscher, A. (2021). On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives. NeuroImage, 243118517. https://doi.org/10.1016/j.neuroimage.2021.118517
Eroğlu, Hasan H, et al. "On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives." NeuroImage vol. 243 (2021): 118517. doi: https://doi.org/10.1016/j.neuroimage.2021.118517
Eroğlu HH, Puonti O, Göksu C, Gregersen F, Siebner HR, Hanson LG, Thielscher A. On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives. Neuroimage. 2021 Nov;243:118517. doi: 10.1016/j.neuroimage.2021.118517. Epub 2021 Sep 01. PMID: 34481368.
Copy Download .nbib Format: NLM
Share it on

Neuroimage. 2021 Nov;243:118517. doi: 10.1016/j.neuroimage.2021.118517. Epub 2021 Sep 01.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives.

NeuroImage

Hasan H Eroğlu, Oula Puonti, Cihan Göksu, Fróði Gregersen, Hartwig R Siebner, Lars G Hanson, Axel Thielscher

Affiliations

  1. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs Lyngby, Denmark.
  2. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
  3. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; High-Field Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
  4. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs Lyngby, Denmark; Sino-Danish Center for Education and Research, Aarhus, Denmark.
  5. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Frederiksberg and Bispebjerg, Copenhagen, Denmark; Department for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark.
  6. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs Lyngby, Denmark. Electronic address: [email protected].

PMID: 34481368 DOI: 10.1016/j.neuroimage.2021.118517

Abstract

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data. We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Copyright © 2021. Published by Elsevier Inc.

Keywords: Hierarchical model selection; Magnetic resonance current density imaging; Magnetic resonance electrical impedance imaging; Projected current density algorithm

Publication Types