An improved 10-tissue human head model with real anatomical structure and hexahedral discretization feature in magnetic induction measurement simulation

Abstract

Magnetic induction measurement (MIM) is a promising technique for biomedical applications in craniocerebral disease detection and monitoring. For the purposes of the MIM simulation, a human head model with real anatomic structure is required. However, nearly anatomically realistic models used in the MIM simulations are discretized using tetrahedral elements of subsequent finite elements method computation. The head model supplied by the Third Military Medical University (TMMU) is currently the only model suitable for hexahedral discretization and finite difference/integral computation. This model has nonetheless number of defects. In order to deal with these, we construct an improved 10-tissue human head model with real anatomical structures and hexahedral discretization features. In this paper, the operation and optimization methods used to construct for the new head model are presented and discussed in detail. We use the 10-tissue head model to conduct the time sensitivity simulation of the magnetic induction sensor which was described in our previous publication and compare the simulation data with those based on the TMMU’s head model. The result shows that the new head model has a higher time sensitivity, meaning better performance in the MIM simulation.

Keywords:

• Magnetic induction measurement
• electromagnetic model
• human head
• anatomical structure
• hexahedral discretization
• simulation

Acknowledgements

The authors would like to thank the Associate Professor X. Ning from the Third Military Medical University and the Engineer H. Li from the No. 161 Hospital of PLA for helpful discussions, and are also grateful to the Third Military Medical University for providing the data source of head model.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Funding

This work was supported by the Innovation Fund for Graduates from the Hunan Province Education Office [grant No. CX2013B011] and the Innovation Fund for Doctoral Candidates from the National University of Defense Technology [grant No. B130205].