  Image contrast in multispectral optoacoustic tomography (MSOT) can be
severely reduced by electrical noise and interference in the acquired
optoacoustic signals. Signal processing techniques have proven insufficient to
remove the effects of electrical noise because they typically rely on
simplified models and fail to capture complex characteristics of signal and
noise. Moreover, they often involve time-consuming processing steps that are
unsuited for real-time imaging applications. In this work, we develop and
demonstrate a discriminative deep learning (DL) approach to separate electrical
noise from optoacoustic signals prior to image reconstruction. The proposed DL
algorithm is based on two key features. First, it learns spatiotemporal
correlations in both noise and signal by using the entire optoacoustic sinogram
as input. Second, it employs training based on a large dataset of
experimentally acquired pure noise and synthetic optoacoustic signals. We
validated the ability of the trained model to accurately remove electrical
noise on synthetic data and on optoacoustic images of a phantom and the human
breast. We demonstrate significant enhancements of morphological and spectral
optoacoustic images reaching 19% higher blood vessel contrast and localized
spectral contrast at depths of more than 2 cm for images acquired in vivo. We
discuss how the proposed denoising framework is applicable to clinical
multispectral optoacoustic tomography and suitable for real-time operation.
