Conditioning Deep Anatomical Prior Knowledge for Reconstruction of Multispectral Optoacoustic Tomography Images

Accurately delineating tissues and reconstructing their chromophore compositions from Multispectral Optoacoustic Tomography (MSOT) images is a key challenge in optoacoustic imaging. The difficulty arises because light fluence distributions within tissue intrinsically depend on spectral optical properties, making the inverse problem inherently ill-posed. Currently, there is a lack of studies leveraging a priori probabilistic anatomical knowledge to guide tissue segmentation and infer chromophore composition. Moreover, most current studies address these two tasks sequentially, which can result in errors accumulating. through the process. To address these issues, we present Anatomical Priors for Reconstruction of Optoacoustic Tomography (APRECOT), a method that leverages probabilistic models of anatomical structures and tissue properties, to enable simultaneous segmentation of tissues and reconstruction of their bulk chromophore compositions. In this proof-of-concept using in-silico data, we show that incorporating probabilistic anatomical context strongly improves the accuracy of bulk chromophore concentration estimation compared to reference methods that do not use any anatomical context or use sequential strategies. This work represents an essential step towards an MSOT imaging mode that directly provides clinically relevant information, such as imaging tissue oxygenation dynamics or disease-related changes in tissue composition.