  Photoacoustic tomography (PAT) has the potential to recover morphological and
functional tissue properties such as blood oxygenation with high spatial
resolution and in an interventional setting. However, decades of research
invested in solving the inverse problem of recovering clinically relevant
tissue properties from spectral measurements have failed to produce solutions
that can quantify tissue parameters robustly in a clinical setting. Previous
attempts to address the limitations of model-based approaches with machine
learning were hampered by the absence of labeled reference data needed for
supervised algorithm training. While this bottleneck has been tackled by
simulating training data, the domain gap between real and simulated images
remains a huge unsolved challenge. As a first step to address this bottleneck,
we propose a novel approach to PAT data simulation, which we refer to as
"learning to simulate". Our approach involves subdividing the challenge of
generating plausible simulations into two disjoint problems: (1) Probabilistic
generation of realistic tissue morphology, represented by semantic segmentation
maps and (2) pixel-wise assignment of corresponding optical and acoustic
properties. In the present work, we focus on the first challenge. Specifically,
we leverage the concept of Generative Adversarial Networks (GANs) trained on
semantically annotated medical imaging data to generate plausible tissue
geometries. According to an initial in silico feasibility study our approach is
well-suited for contributing to realistic PAT image synthesis and could thus
become a fundamental step for deep learning-based quantitative PAT.
