Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence–enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning–based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.

高速可视化大型三维视野中的动态过程对于生命科学中的许多应用至关重要。光场显微镜 (LFM) 已成为快速获取体积图像的工具，但其有效吞吐量和在生物学中的广泛使用受到计算要求高且容易出现伪影的图像重建过程的阻碍。在这里，我们提出了一个人工智能增强显微镜的框架，集成了混合光场光片显微镜和基于深度学习的体积重建。在我们的方法中，同时获得的高分辨率二维光片图像连续用作卷积神经网络在扩展体积延时成像实验期间重建原始 LFM 数据的训练数据和验证。我们的网络以视频速率吞吐量提供高质量的 3D 重建，可以根据高分辨率光片图像进一步细化。我们通过以高达 100 Hz 的体积成像率对青鳉心脏动力学和斑马鱼神经活动进行成像来展示我们方法的能力。