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

Juliane Dinse, Miriam Waehnert, Christine Lucas Tardif, Andreas Schäfer, Stefan Geyer, Robert Turner, Pierre Louis Bazin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages51-58
Number of pages8
EditionPART 2
DOIs
StatePublished - Oct 24 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8150 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

Keywords

  • cortical parcellation
  • cytoarchitetcure
  • myeloarchitecture
  • ultra-high resolution MRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Dinse, J., Waehnert, M., Tardif, C. L., Schäfer, A., Geyer, S., Turner, R., & Bazin, P. L. (2013). A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 51-58). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_7