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Image contrast in multispectral optoacoustic tomography (MSOT) can beseverely reduced by electrical noise and interference in the acquiredoptoacoustic signals. Signal processing techniques have proven insufficient toremove the effects of electrical noise because they typically rely onsimplified models and fail to capture complex characteristics of signal andnoise. Moreover, they often involve time-consuming processing steps that areunsuited for real-time imaging applications. In this work, we develop anddemonstrate a discriminative deep learning (DL) approach to separate electricalnoise from optoacoustic signals prior to image reconstruction. The proposed DLalgorithm is based on two key features. First, it learns spatiotemporalcorrelations in both noise and signal by using the entire optoacoustic sinogramas input. Second, it employs training based on a large dataset ofexperimentally acquired pure noise and synthetic optoacoustic signals. Wevalidated the ability of the trained model to accurately remove electricalnoise on synthetic data and on optoacoustic images of a phantom and the humanbreast. We demonstrate significant enhancements of morphological and spectraloptoacoustic images reaching 19% higher blood vessel contrast and localizedspectral contrast at depths of more than 2 cm for images acquired in vivo. Wediscuss how the proposed denoising framework is applicable to clinicalmultispectral optoacoustic tomography and suitable for real-time operation.

Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue