Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at once, which reduces imaging speed. Furthermore, there is substantial experimental complexity in setting up SIM systems, preventing a widespread adoption. Here, we present Machine-learning Assisted, Interferometric Structured Illumination Microscopy, MAI-SIM, as an easy-to-implement method for live cell super-resolution imaging at high speed and in multiple colors. The instrument is based on an interferometer design in which illumination patterns are generated, rotated, and stepped in phase through movement of a single galvanometric mirror element. The design is robust, flexible, and works for all wavelengths. We complement the unique properties of the microscope with an open source machine-learning toolbox that permits real-time reconstructions to be performed, providing instant visualization of super-resolved images from live biological samples.

结构照明显微镜 (SIM) 是最强大的光学成像方法之一，可用于以亚细胞分辨率可视化生物环境。其局限性在于难以同时在多个颜色通道中成像，从而降低了成像速度。此外，设置 SIM 系统的实验非常复杂，阻碍了广泛采用。在这里，我们介绍机器学习辅助的干涉结构照明显微镜（MAI-SIM），作为一种易于实施的方法，用于高速、多种颜色的活细胞超分辨率成像。该仪器基于干涉仪设计，其中通过单个检流镜元件的移动来生成、旋转和同相步进照明图案。该设计坚固、灵活、并且适用于所有波长。我们通过开源机器学习工具箱补充了显微镜的独特特性，该工具箱允许进行实时重建，从而提供来自活体生物样本的超分辨率图像的即时可视化。