Berger Constantin, Kim Myeongseop, Scheel-Platz Lukas, Eigenberger Andreas, Prantl Lukas, Liu Panhang, Gujrati Vipul, Ntziachristos Vasilis, Jüstel Dominik, Pleitez Miguel A
Institute of Biological and Medical Imaging, Bioengineering Center, Helmholtz Zentrum München, Neuherberg, Germany.
Institute of Computational Biology, Bioengineering Center, Helmholtz Zentrum München, Neuherberg, Germany.
Sci Adv. 2025 Aug 22;11(34):eadu7319. doi: 10.1126/sciadv.adu7319.
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.
高光谱光声显微镜（OAM）能够获取具有无标记生物分子对比度的图像，作为一种诊断工具，为评估新鲜切除且未处理的生物样本提供了极佳的前景。然而，目前耗时的光栅扫描成像方式限制了OAM在临床环境中的应用潜力，例如在术中组织病理学评估中，切除组织的显微照片需要在几分钟内拍摄完成，以便进行快速临床决策。在此，我们提出了一种非数据驱动的计算框架，旨在通过快速数据采集和基于模型的图像重建实现快速OAM，称为贝叶斯光栅计算光声显微镜（BayROM）。与数据驱动方法不同，BayROM不需要训练数据集，而是使用基于概率模型的重建来促进快速高分辨率成像。我们表明，BayROM平均获取显微照片的速度比传统光栅扫描显微镜快10倍，并且提供了足够的图像质量，便于对用于自体脂肪移植的处理后的脂肪移植物进行术中组织学评估。
