Abstract #0370

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

Ilyes Benslimane¹, Günther Grabner², Simon Hametner³, Thomas Jochmann^1,4, Robert Zivadinov^1,5, and Ferdinand Schweser^1,5

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

Synopsis

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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