Efficient photoacoustic image synthesis with Fourier neural operator networks

Tom Rix; Kris K. Dreher; Melanie Schellenberg; Jan-Hinrich Nölke; Alexander Seitel; Lena Maier-Hein

doi:10.1117/12.3001800

13 March 2024 Efficient photoacoustic image synthesis with Fourier neural operator networks

Tom Rix, Kris K. Dreher, Melanie Schellenberg, Jan-Hinrich Nölke, Alexander Seitel, Lena Maier-Hein

Proceedings Volume PC12842, Photons Plus Ultrasound: Imaging and Sensing 2024; PC128422U (2024) https://doi.org/10.1117/12.3001800
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Data-driven approaches to the quantification problem in photoacoustic imaging have shown great potential in silico, but the inherent lack of labelled ground truth data in vivo currently restricts their application and translation into clinics. In this study we leverage Fourier Neural Operator networks as surrogate models to synthesize multispectral photoacoustic human forearm images in order to replace time-consuming and not inherently differentiable state-of-the-art Monte Carlo and k-Wave simulations. We investigate the accuracy and efficiency of these surrogate models for the optical and acoustic simulation step.

Citation Download Citation

Tom Rix, Kris K. Dreher, Melanie Schellenberg, Jan-Hinrich Nölke, Alexander Seitel, and Lena Maier-Hein "Efficient photoacoustic image synthesis with Fourier neural operator networks", Proc. SPIE PC12842, Photons Plus Ultrasound: Imaging and Sensing 2024, PC128422U (13 March 2024); https://doi.org/10.1117/12.3001800

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