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Quantitative magnetic resonance imaging of brain anatomy and in vivo histology

MPG-Autoren
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Weiskopf,  Nikolaus
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Felix Bloch Institute for Solid State Physics, University of Leipzig, Germany;

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Edwards,  Luke
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Helms,  Gunther
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Clinical Sciences, Lund University, Sweden;

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Mohammadi,  Siawoosh
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany;

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Kirilina,  Evgeniya
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Center for Cognitive Neuroscience Berlin (CCNB), FU Berlin, Germany;

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Zitation

Weiskopf, N., Edwards, L., Helms, G., Mohammadi, S., & Kirilina, E. (2021). Quantitative magnetic resonance imaging of brain anatomy and in vivo histology. Nature Reviews Physics. doi:10.1038/s42254-021-00326-1.


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-D9A8-6
Zusammenfassung
Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which aims primarily at local image contrast. It provides specific physical parameters related to the nuclear spin of protons in water, such as relaxation times. These parameters carry information about the local microstructural environment of the protons (such as myelin in the brain). Non-invasive in vivo histology using MRI (hMRI) aims to use this information to directly characterize biological tissue microstructure, partially replacing or complementing classical invasive histology. The understanding of MRI tissue contrast provided by hMRI is, in turn, crucial for further improvements of qMRI, and they should be considered closely interlinked. We discuss concepts, models and validation approaches, pointing out challenges and the latest advances in this field. Further, we point out links to physics, including computational and analytical approaches and developments in materials science and photonics, that aid in reference data acquisition and model validation.