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Abstract #0762

Microstructure-Informed Synthetic Axons (MISA): generating synthetic axons from 3D histology

Mariam Andersson1,2,3, Jonathan Rafael-Patino1,4,5, Hans Martin Martin Kjer2,3, Marco Pizzolato2,3,4, Gabriel Girard4,5,6, Jean-Philippe Thiran4,5,6, and Tim B. Dyrby2,3
1* (Equal contributions), Lausanne, Switzerland and Copenhagen, Denmark, 2Copenhagen University Hospital - Amager and Hvidovre, Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark, 3Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark, 4Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 5Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, 6CIBM Center for Biomedical Imaging, Lausanne, Switzerland


Changes in diffusion properties of the intra-axonal space can be linked to brain health and function. Monte Carlo simulations of diffusion using realistic substrates are important for sequence design and for understanding diffusion MRI signals observed ex vivo and in vivo. However, it is difficult to construct tissue phantoms that accurately reflect compartment morphology. Here, we present the framework MISA for the generation of Microstructure-Informed Synthetic Axons. MISA uses statistical descriptions of axonal morphology from 3D histology to mimic real axons. We show here that MISA axons match axons from 3D histology in terms of their diffusion properties.

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