Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging

Abstract

Multispectral photoacoustic imaging (PAI) is an emerging imaging modality that enables the recovery of functional tissue parameters such as blood oxygenation. However, the underlying inverse reconstruction 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 using conditional invertible neural networks. We propose going beyond commonly used point estimates for tissue oxygenation and convert 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 that 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.