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Brain tissues have ...
Brain tissues have single-voxel signatures in multi-spectral MRI
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- German, Alexander (author)
- University Hospital Erlangen
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- Mennecke, Angelika (author)
- University Hospital Erlangen
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- Martin, Jan (author)
- Lund University,Lunds universitet,Fysikalisk kemi,Enheten för fysikalisk och teoretisk kemi,Kemiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Physical Chemistry,Physical and theoretical chemistry,Department of Chemistry,Departments at LTH,Faculty of Engineering, LTH,University Hospital Erlangen
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- Hanspach, Jannis (author)
- University Hospital Erlangen
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- Liebert, Andrzej (author)
- University Hospital Erlangen
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- Herrler, Jürgen (author)
- University Hospital Erlangen
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- Kuder, Tristan Anselm (author)
- German Cancer Research Centre
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- Schmidt, Manuel (author)
- University Hospital Erlangen
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- Nagel, Armin (author)
- University Hospital Erlangen
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- Uder, Michael (author)
- University Hospital Erlangen
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- Doerfler, Arnd (author)
- University Hospital Erlangen
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- Winkler, Jürgen (author)
- University Hospital Erlangen
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- Zaiss, Moritz (author)
- Max Planck Institute for Biological Cybernetics,University Hospital Erlangen
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- Laun, Frederik Bernd (author)
- University Hospital Erlangen
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(creator_code:org_t)
- Elsevier BV, 2021
- 2021
- English.
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In: NeuroImage. - : Elsevier BV. - 1053-8119. ; 234
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Abstract
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- Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues – and other tissues – based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Keyword
- Brain
- Data Analysis
- High-Field Imaging
- Machine Learning
- MRI
- Segmentation
Publication and Content Type
- art (subject category)
- ref (subject category)
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NeuroImage
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- By the author/editor
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German, Alexande ...
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Mennecke, Angeli ...
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Martin, Jan
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Hanspach, Jannis
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Liebert, Andrzej
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Herrler, Jürgen
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show more...
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Kuder, Tristan A ...
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Schmidt, Manuel
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Nagel, Armin
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Uder, Michael
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Doerfler, Arnd
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Winkler, Jürgen
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Zaiss, Moritz
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Laun, Frederik B ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Radiology Nuclea ...
- Articles in the publication
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NeuroImage
- By the university
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Lund University