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Apr 08, 2024 98 likes •363 views

A histology-based model of quantitative T1 contrast for in-vivo cortical parcellation of high-resolution 7 Tesla brain MR images J. Dinse 1,2 , M. Waehnert 1 , C. L. Tardif 1 , A. Schfer 1 , S. Geyer 1 , R. Turner 1 , P.-L. Bazin 1 Presented by

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

1 Max Planck Institute for Human Cognitive and Brain Sciences,

Leipzig, Germany

2 Simulation and Graphics Department, Faculty of Computer Science,

Otto-von-Guericke University Magdeburg, Germany

  • J. Dinse1,2, M. Waehnert1, C. L. Tardif1, A. Schäfer1,
  • S. Geyer1, R. Turner1, P.-L. Bazin1

Presented by Juliane Dinse

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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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Cortical anatomy and cytoarchitecture

Cytoarchitecture, Vogt, 1903 Brodmann‘s Map, 1909, lateral view (43 areas) Atlas of von Economo and Koskinas, 1925, (107 areas in total, 40 areas quantified)

Cytoarchitectonic m apping of Brodm ann Areas ( BA) is accepted as standard reference.

Human brain

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Myeloarchitecture

Myeloarchitecture Vogt, 1903 Incomplete myelo- architectonic map, Vogt & Vogt, 1910, frontal pole

?

Research in this field is incom plete, inconclusive

  • r even contradictory.

Myeloarchitectonic map, E. Smith, 1907, lateral view

Cell stain Myelin stain

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Motivation

Cytoarchitecture Myeloarchitecture

T1 m ap, 0 .5 3 m m ,

courtesy of M. Waehnert, 2013

  • Cytoarchitecture can be transformed into information regarding relative cortical

myelin density

  • Cortical myelin provides MRI contrast: Enables segregation of primary

areas based on cortical profiles (Geyer et al., 2011; Dinse et al., 2013)

Cortical depth Hellwig, 1993

Model

1400 2400 T1 (ms)

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Is there a way of mapping myeloarchitecture in-vivo onto the human cortical surface?

Step 1 Step 2

BA 3b, layer IIIc: Thick (%): 10 Cells: 30 Cell Size: 17

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

General assumptions

Assum ption I : Cell size is proportional to myelin concentration. Assum ption I I : Horizontal pattern originates from axonal collaterals of cells. (Paldino &

Harth, 1975)

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Step1: Generate myelin density profiles

  • Obtain quantitative measures of cellular configuration of each cortical

layer in each ROI (von Economo & Koskinas, 1925)

  • Link measures to assumption I
  • First estimate of myelin density
  • Convolve graph with model given in assumption II (Paldino, 1975)
  • Qualitative indicator of myelin concentration in our ROIs

* * =

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Step 2: Normalization to T1 contrast

* =

  • Define individual range of T1 values for each ROI
  • Normalize profiles into T1 contrast of gray matter (Rooney et al., 2007)
  • Convolve with Lorentzian kernel to account for MR limiting effects

(partial voluming and resolution)

  • Quantitative indicator of myelin concentration in our ROIs
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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Comparison between model and data

BA 4

BA 3b BA 1 BA 2

Empirical Data Modelled Profile MR adjusted Mod. Profile

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Probabilistic Model

Scaling factor Com parison Data vs Model W eighting Fct

probability frequency probability frequency

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Data Acquisition

  • 9 subjects scanned with a 7 Tesla scanner and MP2RAGE sequence

Marques et al., 2010; Hurley et al., 2010

  • 0.5 mm isotropic T1 map with strong intra-cortical contrast in ROIs

4 3 b 1 2 Brodmann Areas 4, 3b, 1 and 2 in primary motor-somatosensory cortex

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Processing

  • Rigid image registration to MNI space at (0.4 mm) 3
  • MGDM whole brain segmentation
  • CRUISE cortical surface extraction

Han et al., NeuroImage 2004; Bazin et al., NeuroImage 2013

  • Cortical layering and profile sampling

Waehnert et al., NeuroImage 2013

  • Manual labelling in ROIs

W M/ GM and GM/ CSF boundaries Cortical layering Manual labels follow ing m acro- anatom ical landm arks in ROI s

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Probabilities on cortical surface

  • If model and area match, probabilities are high
  • Surfaces show inconsistent patterns w hen m odel and area do

not m atch

  • More details are on my poster (Wednesday, 2 – 4.30 pm, O4-01)

BA 4

1

4 3 b 1 2

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

BA 4 BA 3 b BA 1 BA 2 BA 4 0.87 (0.55) 0.0 (0.02) 0.83 (0.31) 0.86 (0.36) BA 3 b 0.71 (0.77) 0.87 (0.26) 0.77 ( 0.45) 0.64 (0.55) BA 1 0.19 (0.63) 0.66 (0.94) 0.89 (0.25) 0.81 (0.42) BA 2 0.68 (0.88) 0.0 (0.01) 0.80 (0.31) 0.83 (0.45) BA 4 BA 3 b BA 1 BA 2 BA 4 0.75 (0.47) 0.67 (0.33) 0.69 (0.31) 0.72 (0.36) BA 3 b 0.59 (0.51) 0.88 (0.26) 0.69 ( 0.47) 0.65 (0.47) BA 1 0.43 (0.52) 0.71 (0.54) 0.73 (0.45) 0.73 (0.39) BA 2 0.62 (0.52) 0.69 (0.31) 0.73 (0.46) 0.70 (0.45) Labelled ROIs Modelled BAs Modelled BAs Single subject Group average

Results on subject- and group level

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

  • Differentiation of closely related cortical functional

areas is possible in in-vivo vo at ultra-high resolution

  • Generative model, which can predict quant

quantitative e T1 m T1 maps aps

  • Prospective motion correction and optimized coils

may help to further increase the image quality

  • For robust and automatic parcellation of many

cortical areas, additional information is needed:

  • spatial priors and regularisation, topological constraints
  • New insights into the relation between

myeloar

  • architec

ectur ure e and cyto toarc rchite tecture re

Summary and Conclusion

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Thanks to: Pierre-Louis Bazin Miriam Waehnert Christine Tardif Andreas Schäfer

  • Prof. Robert Turner

Stefan Geyer Enrico Reimer Katja Reimann

http:/ / m ipav.cit.nih.gov/

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Dōmo arigatō

Poster: Wednesday, 2 – 4.30 pm O4-01

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Model vs. Resolution

Images: courtesy of Nina Härtwich1,2

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

T1 map atlas and qSM atlas

22 subjects, (0.5 mm) 3 10 subjects, (0.5 mm) 3

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Post-mortem analysis: MRI

Superimposed Layering 1 2 3b 4 6 MP2RAGE, T1 map, 2003 μm covering our ROIs

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Post-mortem analysis: histology

MP2RAGE, T1 map, 2003 μm Myelin stain, 2.5 μm 1 2 3b 4 6 1 2 3b 4 6

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Model vs. Histology: BA 2

Images: courtesy of Nina Härtwich1,2

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Model vs. Histology: res 0.05 mm

Images: courtesy of Nina Härtwich1,2

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Model vs. Histology: res 0.5 mm

Images: courtesy of Nina Härtwich1,2

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Juliane Dinse Max Planck Institute for Human Cognitive and Brain Sciences

Model vs. Histology: res 1 mm

Images: courtesy of Nina Härtwich1,2