We present a structured illumination microscopy system that projects a hexagonal pattern by the interference among three coherent beams, suitable for implementation in a light-sheet geometry. Seven images acquired as the illumination pattern is shifted laterally can be processed to produce a super-resolved image that surpasses the diffraction-limited resolution by a factor of over 2 in an exemplar light-sheet arrangement. Three methods of processing data are discussed depending on whether the raw images are available in groups of seven, individually in a stream or as a larger batch representing a three-dimensional stack. We show that imaging axially moving samples can introduce artefacts, visible as fine structures in the processed images. However, these artefacts are easily removed by a filtering operation carried out as part of the batch processing algorithm for three-dimensional stacks. The reconstruction algorithms implemented in Python include specific optimizations for calculation on a graphics processing unit and we demonstrate its operation on experimental data of static objects and on simulated data of moving objects. We show that the software can process over 239 input raw frames per second at 512 × 512 pixels, generating over 34 super-resolved frames per second at 1024 × 1024 pixels.

This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

我们提出了一种结构化照明显微镜系统，该系统通过三个相干光束之间的干涉投射出六边形图案，适用于在光片几何结构中实现。可以处理随着照明图案横向移动而获得的七幅图像，以产生超分辨图像，该图像在示例光片布置中超过衍射极限分辨率的 2 倍以上。讨论了三种处理数据的方法，具体取决于原始图像是否以七组的形式提供，单独以流形式提供，还是作为代表三维堆栈的较大批次提供。我们表明，对轴向移动样本进行成像可以引入伪影，在处理后的图像中可以看到精细结构。然而，这些伪影很容易通过作为三维堆栈批处理算法的一部分执行的过滤操作来去除。用 Python 实现的重建算法包括对图形处理单元计算的特定优化，我们演示了它对静态对象的实验数据和运动对象的模拟数据的操作。我们展示了该软件每秒可以处理超过 239 个 512 × 512 像素的输入原始帧，每秒生成超过 34 个 1024 × 1024 像素的超分辨率帧。

本文是 Theo Murphy 会议问题“超分辨率结构照明显微镜（第 1 部分）”的一部分。