Abstract

Over the last decade, Razansky lab was instrumental in the development of multi-spectral optoacoustic tomography (MSOT), transforming this novel bio-imaging technology from the initial demonstration of technical feasibility, through establishment of image reconstruction methodologies all the way toward its clinical translation. The method rapidly finds its place as a potent clinical imaging tool due to its high sensitivity and molecular specificity as well as non-invasive, real-time and high-resolution volumetric imaging capabilities deep in living biological tissues. Despite great promise demonstrated in the pilot clinical studies, human imaging with MSOT is afflicted by a limited tomographic access to the region of interest while significant constraints are further imposed on the light deposition in deep tissues. This project aims at development of new artificial intelligence capabilities for improving image quality and diagnostic capacity of MSOT images acquired by sub-optimal scanner configurations resulting from e.g. application-related constraints or low cost design considerations. In particular, we will devise machine learning approaches to enable efficient and robust multimodal combination of MSOT with pulse-echo ultrasonography by training neural networks on high-resolution and -quality training datasets generated by dedicated optimally designed scanner configurations. The trained models will be used to restore quality of artifactual images produced by various sub-optimal scanner configurations with limited tomographic view or sparsely acquired data in typical clinical imaging scenarios. Those advancements will help reducing inter-clinician variability and enable a more efficient, rapid, and objective analysis of large amounts of image data, thus relaxing requirements for specialized training and facilitating the wider adoption of MSOT apparatus in primary care and other non-hospital settings.