  Optoacoustic (OA) imaging is based on excitation of biological tissues with
nanosecond-duration laser pulses followed by subsequent detection of ultrasound
waves generated via light-absorption-mediated thermoelastic expansion. OA
imaging features a powerful combination between rich optical contrast and high
resolution in deep tissues. This enabled the exploration of a number of
attractive new applications both in clinical and laboratory settings. However,
no standardized datasets generated with different types of experimental set-up
and associated processing methods are available to facilitate advances in
broader applications of OA in clinical settings. This complicates an objective
comparison between new and established data processing methods, often leading
to qualitative results and arbitrary interpretations of the data. In this
paper, we provide both experimental and synthetic OA raw signals and
reconstructed image domain datasets rendered with different experimental
parameters and tomographic acquisition geometries. We further provide trained
neural networks to tackle three important challenges related to OA image
processing, namely accurate reconstruction under limited view tomographic
conditions, removal of spatial undersampling artifacts and anatomical
segmentation for improved image reconstruction. Specifically, we define 44
experiments corresponding to the aforementioned challenges as benchmarks to be
used as a reference for the development of more advanced processing methods.
