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A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images

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Dinse,  Juliane
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany;

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Wähnert,  Miriam
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Tardif,  Christine
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schäfer,  Andreas
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Geyer,  Stefan
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Turner,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Bazin,  Pierre-Louis
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Dinse, J., Wähnert, M., Tardif, C., Schäfer, A., Geyer, S., Turner, R., et al. (2013). A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. In K. Mori, I. Sakuma, Y. Sato, C. Barillot, & N. Navab (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2013 (pp. 51-58). Berlin: Springer. doi:10.1007/978-3-642-40763-5_7.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-7738-7
Abstract
A conclusive mapping of myeloarchitecture (myelin patterns) onto the cortical sheet and, thus, a corresponding mapping to cytoarchitecture (cell configuration) does not exist today. In this paper we present a generative model which can predict, on the basis of known cytoarchitecture, myeloarchitecture in different primary and non-primary cortical areas, resulting in simulated in-vivo quantitative T1 maps. The predicted patterns can be used in brain parcellation. Our model is validated using a similarity distance metric which enables quantitative comparison of the results with empirical data measured using MRI. The work presented may provide new perspectives for this line of research, both in imaging and in modelling the relationship with myelo- and cytoarchitecture, thus leading the way towards in-vivo histology using MRI.