Bayesian raster-computed optoacoustic microscopy (BayROM) enables fast, label-free hyperspectral imaging

Optoacoustic microscopy (OAM) provides excellent capabilities to facilitate fast intraoperative decision-making based on label-free chemical imaging. However, most raster-scanning OAM modalities suffer from time-consuming image acquisition. To overcome the limitations of OAM associated with raster scanning, we developed a non-data-driven computational method for sparse raster scanning combined with Bayesian image reconstruction, termed BayROM. In the context of hyperspectral imaging, we demonstrate that BayROM can image 10 times faster than full raster scanning. BayROM could facilitate the translation of OAM into clinical label-free histology by enabling fast intra-operative decision-making in applications such as tumor margin assessment.