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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages51-58
Number of pages8
Volume8150 LNCS
EditionPART 2
DOIs
StatePublished - 2013
Externally publishedYes
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Histology
Magnetic resonance imaging
Brain
High Resolution
Generative Models
Distance Metric
Imaging techniques
Imaging
Model
Predict
Configuration
Line
Cell
Modeling

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 8150 LNCS, 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

A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. / Dinse, Juliane; Waehnert, Miriam; Tardif, Christine Lucas; Schäfer, Andreas; Geyer, Stefan; Turner, Robert; Bazin, Pierre Louis.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8150 LNCS PART 2. ed. 2013. p. 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).

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

Dinse, J, Waehnert, M, Tardif, CL, Schäfer, A, Geyer, S, Turner, R & Bazin, PL 2013, A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 8150 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8150 LNCS, pp. 51-58, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-642-40763-5_7
Dinse J, Waehnert M, Tardif CL, Schäfer A, Geyer S, Turner R et al. A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 8150 LNCS. 2013. p. 51-58. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-40763-5_7
Dinse, Juliane ; Waehnert, Miriam ; Tardif, Christine Lucas ; Schäfer, Andreas ; Geyer, Stefan ; Turner, Robert ; Bazin, Pierre Louis. / A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8150 LNCS PART 2. ed. 2013. pp. 51-58 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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