Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
This work addresses uncertainty quantification for researchers in photoacoustic imaging, offering a novel method to handle ambiguity in recovering physiological parameters, though it is incremental as it builds on existing invertible neural network concepts.
The paper tackled the ill-posed inverse problem in multispectral photoacoustic imaging by using conditional invertible neural networks to estimate full posterior probability densities for tissue oxygenation, enabling detection, quantification, and compensation of uncertainties in device design and image acquisition.
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.