Efficient photoacoustic image synthesis with Fourier neural operator networks

Résumé fourni par la source

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.

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