  Multispectral photoacoustic imaging (PAI) is an emerging imaging modality
which enables the recovery of functional tissue parameters such as blood
oxygenation. However, the underlying inverse problems are potentially
ill-posed, meaning that radically different tissue properties may - in theory -
yield comparable measurements. In this work, we present a new approach for
handling this specific type of uncertainty by leveraging the concept of
conditional invertible neural networks (cINNs). Specifically, we propose going
beyond commonly used point estimates for tissue oxygenation and converting
single-pixel initial pressure spectra to the full posterior probability
density. This way, the inherent ambiguity of a problem can be encoded with
multiple modes in the output. Based on the presented architecture, we
demonstrate two use cases which leverage this information to not only detect
and quantify but also to compensate for uncertainties: (1) photoacoustic device
design and (2) optimization of photoacoustic image acquisition. Our in silico
studies demonstrate the potential of the proposed methodology to become an
important building block for uncertainty-aware reconstruction of physiological
parameters with PAI.
