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Abstract #0370

Beyond qMRI: Biological tissue properties from single-subject unsupervised deep learning with theoretical signal constraints

Ilyes Benslimane1, Günther Grabner2, Simon Hametner3, Thomas Jochmann1,4, Robert Zivadinov1,5, and Ferdinand Schweser1,5
1Department of Neurology, Buffalo Neuroimaging Analysis Center, Buffalo, NY, United States, 2Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria, 3Department of Biomedical Imaging and Image-Guided Therapy, High Field MR Centre, Medical University of Vienna, Austria, 4Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, 5Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States


Most quantitative MRI (qMRI) parameters co-depend on multiple tissue properties, limiting their clinical value. Here, we introduce a biophysically constrained autoencoder network that accepts multiparametric MR data and outputs underlying biological tissue parameters. The method requires no ground truth data, no knowledge of the (generally unknown) biophysical model parameters, and a single subject provides sufficient training data. We validated the method using ex vivo iron and myelin stains.

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