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Semi-automated generation of individual computational models of the human head and torso from MR images

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Kalloch,  Benjamin
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
University of Applied Sciences, Leipzig, Germany;

Bode,  Jens
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Engineering Physics, University of Applied Sciences Münster, Germany;

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Kozlov,  Mikhail
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Pampel,  André
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Sehm,  Bernhard
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Bazin,  Pierre-Louis
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
The Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands;
Spinoza Centre for Neuroimaging, University of Amsterdam, the Netherlands;

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Citation

Kalloch, B., Bode, J., Kozlov, M., Pampel, A., Hlawitschka, M., Sehm, B., et al. (2019). Semi-automated generation of individual computational models of the human head and torso from MR images. Magnetic Resonance in Medicine, 81(3), 2090-2105. doi:10.1002/mrm.27508.


Cite as: http://hdl.handle.net/21.11116/0000-0002-4EE7-3
Abstract
PURPOSE: Simulating the interaction of the human body with electromagnetic fields is an active field of research. Individualized models are increasingly being used, as anatomical differences affect the simulation results. We introduce a processing pipeline for creating individual surface-based models of the human head and torso for application in simulation software based on unstructured grids. The pipeline is designed for easy applicability and is publicly released on figshare. METHODS: The pipeline covers image acquisition, segmentation, generation of segmentation masks, and surface mesh generation of the single, external boundary of each structure of interest. Two gradient-echo sequences are used for image acquisition. Structures of the head and body are segmented using several atlas-based approaches. They consist of bone/skull, subarachnoid cerebrospinal fluid, gray matter, white matter, spinal cord, lungs, the sinuses of the skull, and a combined class of all other structures including skin. After minor manual preparation, segmentation images are processed to segmentation masks, which are binarized images per segmented structure free of misclassified voxels and without an internal boundary. The proposed workflow is applied to 2 healthy subjects. RESULTS: Individual differences of the subjects are well represented. The models are proven to be suitable for simulation of the RF electromagnetic field distribution. CONCLUSION: Image segmentation, creation of segmentation masks, and surface mesh generation are highly automated. Manual interventions remain for preparing the segmentation images prior to segmentation mask generation. The generated surfaces exhibit a single boundary per structure and are suitable inputs for simulation software.