0330
View Abstract / View Presentation
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 Zivadinov1,3, and Ferdinand Schweser1,3
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
The physics-informed network successful demonstrated the capacity to quickly produce accurate B0 and R2* field maps without phase wrapping artifacts and with typical contrast variations compared to those produced with conventional methods.
Figure 3: Shown from top left to bottom right: (a) Conventionally obtained amplitude image of a multi-echo GRE scan, (b) Network predicted amplitude parameter map, (c) Ratio of predicted to conventionally obtained amplitude images, (d) Conventionally obtained R2* image of a multi-echo GRE scan, (e) Network predicted R2* parameter map, (f) Difference in R2* maps between conventionally obtained and network trained methods, (g) the frequency prediction map, (h) the phase offset prediction, (i) the gradient offset prediction
Figure 2: Shown from top left to bottom right: (a) predicted magnitude image of the 10th echo of the multi-echo GRE scan (at network output layer), (b) expected magnitude image of the input multi-echo GRE scan, (c) magnitude image discrepancy, (d) predicted phase image, (e) expected input phase image, (f) phase image discrepancy.