Abstract #2359

MR Vascular Fingerprinting with 3D realistic blood vessel structures and machine learning to assess oxygenation changes in human volunteers

Aurélien Delphin¹, Thomas Coudert¹, Audrey Fan², Michael E. Moseley³, Greg Zaharchuk³, and Thomas Christen¹

¹Univ. Grenoble Alpes, INSERM U1216, Grenoble Institut Neurosciences, GIN, Grenoble, France, ²Biomedical Engineering, University of California Davis, Davis, CA, United States, ³Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: MR Fingerprinting/Synthetic MR, OxygenationThe MR vascular fingerprinting (MRvF) approach extends the concept of MR fingerprinting to the study of microvascular properties and functions. Encouraging results have been obtained in healthy human volunteers as well as in stroke and tumor models in rats. However, it has been suggested that the method has a low sensitivity to blood oxygenation measurements. We improved the MRvF approach by using simulations with 3D realistic blood vessels from animal microscopy, new fingerprint-pattern organization and machine learning tools. The method was tested in retrospective data acquired in healthy-human volunteers while breathing different gas mixtures (Hyperoxia (100%O₂), Normoxia (21%O₂), hypoxia (14%O₂)).

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