Abstract #0330

Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems – Relaxometry and field mapping from multi-echo data

Ilyes Benslimane¹, Thomas Jochmann², Robert Zivadinov^1,3, and Ferdinand Schweser^1,3

¹Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, Buffalo, NY, United States, ²Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, Jena, Thuringia, Germany, ³Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, USA, Buffalo, NY, United States

Modeling the non-linear relationship of the Magnetic Resonance (MR) signal and biophysical sources is computationally expensive and unstable using conventional methods. We develop an unsupervised physics-informed deep learning algorithm that quantifies MR parameters from multi-echo GRE data in a single computational pass. The algorithm produced accurate B₀ and R₂* field maps without phase wrapping artifacts and with typical contrast variations. The success of this network demonstrates the feasibility of physics-informed quantitative MRI (qMRI) without the need for ground truth training data, typically required by similar networks. This developed tool could provide fast and comprehensive tissue characterization in qMRI.

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