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This work was supported in part by the Breast Cancer Research Foundation (BCRF-22-043) and the National Institutes of Health (EB032822 and R01NS117613). In addition, Anthony Yu is funded in part by the US Army Medical Research and Development Command (Award W81XWH-19-1-0187). (Corresponding author: Stanislav Emelianov)This work involved human subjects or animals in its research. All experiments involving animals were conducted with the approval of the Institutional Animal Care and Use Committee at the Georgia Institute of Technology.Sarkis Ter Martirosyan was with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States while working on this project. He is now with the Institute of Biological and Medical Imaging, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany as well as the Chair of Biological Imaging, Technische Universität München, 80333 München, Germany. (e-mail: s.ter-martirosyan@tum.de).Xinyue Huang, David Qin, and Anthony Yu are with The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, United States. (e-mails: xinyue@gatech.edu, dqin@gatech.edu, anthony.yu@gatech.edu)Stanislav Emelianov is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States as well as The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, United States (e-mail: stas@gatech.edu)
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Physics-informed neural networks can crack the notoriously difficult problem of spectral unmixing in photoacoustic imaging, even without ground truth data for chromophore concentrations.