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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-4EE7-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DBB9-5
Genre: Journal Article

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 Creators:
Kalloch, Benjamin1, 2, Author              
Bode, Jens 1, 3, Author
Kozlov, Mikhail4, Author              
Pampel, André5, Author              
Hlawitschka, Mario 2, Author
Sehm, Bernhard1, Author              
Villringer, Arno1, Author              
Möller, Harald E.5, Author              
Bazin, Pierre-Louis4, 6, 7, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2University of Applied Sciences, Leipzig, Germany, ou_persistent22              
3Department of Engineering Physics, University of Applied Sciences Münster, Germany, ou_persistent22              
4Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
5Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
6The Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands, ou_persistent22              
7Spinoza Centre for Neuroimaging, University of Amsterdam, the Netherlands, ou_persistent22              

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Free keywords: MR safety; RF simulations; atlas-based segmentation; computational modeling; finite element method; human head models
 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.

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Language(s): eng - English
 Dates: 2018-07-052018-04-062018-08-042018-09-192019-03
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1002/mrm.27508
PMID: 30230021
Other: Epub 2018
 Degree: -

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Funding organization : FAZIT‐STIFTUNG
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Funding organization : International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom)

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Title: Magnetic Resonance in Medicine
Source Genre: Journal
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Publ. Info: New York : Wiley-Liss
Pages: - Volume / Issue: 81 (3) Sequence Number: - Start / End Page: 2090 - 2105 Identifier: ISSN: 0740-3194
CoNE: https://pure.mpg.de/cone/journals/resource/954925538149