Bayesian reconstruction of rapidly scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy.

Hyperspectral optoacoustic microscopy (OAM) enables obtaining images with label-free biomolecular contrast, offering excellent perspectives as a diagnostic tool to assess freshly excised and unprocessed biological samples. However, time-consuming raster scanning image formation currently limits the translation potential of OAM into the clinical setting, for instance, in intraoperative histopathological assessments, where micrographs of excised tissue need to be taken within a few minutes for fast clinical decision-making. Here, we present a non-data-driven computational framework tailored to enable fast OAM by rapid data acquisition and model-based image reconstruction, termed Bayesian raster-computed optoacoustic microscopy (BayROM). Unlike data-driven approaches, BayROM does not require training datasets, but instead, it uses probabilistic model-based reconstruction to facilitate fast high-resolution imaging. We show that BayROM enables acquiring micrographs 10 times faster on average than conventional raster scanning microscopy and provides sufficient image quality to facilitate the intraoperative histological assessment of processed fat grafts for autologous fat transfer.