Principle Investigator (PI)

Prof. Dr. Nikolaus Weiskopf
Prof. Dr. Nikolaus Weiskopf
Director
Phone: +49 341 9940-133

Responsible Scientists

Dr. Luke Edwards
Dr. Luke Edwards
Postdoc
Phone: +49 341 9940-2428
Dr. Kerrin Pine
Scientific researcher
Phone: +49 341 9940-2291

Department of Neurophysics

hMRI: Non-Invasive In-Vivo Histology in Health and Disease Using Magnetic Resonance Imaging (MRI)

Extracting histological information from MRI acquisitions

The aim of the ERC-funded histology-MRI (hMRI) project, shown schematically in the image below, is to be able to extract detailed information about the cortex of the human brain, currently available only from invasive ex vivo histology, in vivo using non-invasive MRI. Potential applications of hMRI are numerous; as just two examples, in vivo histology may allow clinicians to diagnose several diseases that can currently only be confirmed post mortem through ex vivo histology, and neuroscientists to directly probe human cortical networks.

hMRI requires a closely intertwined combination of state-of-the-art MRI techniques, biophysical models, and image-processing.

  1. MRI acquisition techniques.
    Development of MRI acquisition is needed to obtain the best data we can, and will be aided both by the development of quantitative MRI acquisitions, and also by top of the line hardware, including a Siemens 7 T high field scanner, a Siemens Connectom scanner, Skope field cameras, and a Kineticor prospective motion system.

  2. Biophysical models.
    Fundamental limits on the resolution of in vivo MRI acquisitions mean that cortical structures such as neurons cannot be imaged directly. Instead, we must use biophysical models to relate measured MRI signals to the underlying average microstructure; inversion of these models then allows the extraction of histological information from the MRI data needed for hMRI. It is thus important to develop appropriate biophysical models, and their development is thus a fundamental arm of the project.

  3. Advanced image processing techniques.
    The last branch of the project is image processing: this is needed both to improve the acquired data, e.g. through the mitigation of image artefacts, incorporation of information from the field cameras, and superresolution techniques, as well as to allow comparison between MRI and classical histology for validation of biophysical models.

hMRI will not be easy to achieve, but we are confident that, with the rapid developments coming from both us and the rest of the MRI community, hMRI will become possible in the near future. We will then be able to reap the rewards: structural information currently only available under a microscope will be accessible in vivo and allow revolutions in the study of both neuropathology and neuroscience.

 
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