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STI and DTI: Tensor Characteristics and a machine-learning approach to estimate susceptibility tensors

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Gkotsoulias,  Dimitrios
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Eichner,  Cornelius
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schlumm,  Torsten
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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

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Pampel,  André
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Gkotsoulias, D., Metere, R., Eichner, C., Schlumm, T., Anwander, A., Jäger, C., et al. (2020). STI and DTI: Tensor Characteristics and a machine-learning approach to estimate susceptibility tensors. Poster presented at Fachbeirat 2020, Virtual.


Cite as: http://hdl.handle.net/21.11116/0000-0007-1F07-0
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