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Abstract #2592

Using 3D realistic blood vessel structures and machine learning for MR vascular Fingerprinting

Aurélien Delphin1, Fabien Boux1,2, Clément Brossard1,3, Jan M Warnking1, Benjamin Lemasson1,3, Emmanuel Luc Barbier1, and Thomas Christen1
1Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France, 2Univ. Grenoble Alpes, Inria, CNRS, G-INP, 38000, Grenoble, France, 3MoGlimaging Network, HTE Program of the French Cancer Plan, Toulouse, France


MR vascular fingerprinting aims at mapping cerebral vascular properties such as blood volume and blood oxygenation. We propose to improve the technique by generating dictionaries based on 3D vascular networks segmented from whole brain high-resolution (3 µm isotropic) microscopy datasets. In order to compensate for the limited number of available data and long computing times, we used a machine-learning reconstruction process to generalize our results and tested our approach in healthy, stroke and tumor animal models. Our results show high quality maps with expected contrast and baseline values in healthy animals as well as expected trends in pathological tissues.

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