美国加州大学伯利克分校Srigokul Upadhyayula等研究人员合作开发出用于拍字节级光片显微镜数据的图像处理工具。相关论文于2024年10月17日在线发表在《自然—方法学》杂志上。

研究人员表示，光片显微镜是一种高效的技术，用于高速度三维成像亚细胞动态和大型生物样本。然而，它通常会为单个实验生成数百GB到PB级的数据集。传统的计算工具处理这些图像的速度远低于获取图像的时间，并且由于内存限制，往往会完全失败。

为了解决这些挑战，研究人员推出了PetaKit5D，这是一种可扩展的软件解决方案，用于高效的PB级光片图像处理。该软件包含一套经过优化的常用处理工具，以提高内存和性能。显著的进展包括快速的图像读取和写入、快速且内存高效的几何变换、高性能的Richardson-Lucy解卷积和可扩展的基于Zarr的拼接。这些功能在性能上超越了最先进的方法，提升了一个数量级，使得能够以现代成像相机的全特拉体素速率处理PB级图像数据。该软件为通过大规模成像实验实现生物发现开辟了新的途径。

附：英文原文

Title: Image processing tools for petabyte-scale light sheet microscopy data

Author: Ruan, Xiongtao, Mueller, Matthew, Liu, Gaoxiang, Grlitz, Frederik, Fu, Tian-Ming, Milkie, Daniel E., Lillvis, Joshua L., Kuhn, Alexander, Gan Chong, Johnny, Hong, Jason Li, Herr, Chu Yi Aaron, Hercule, Wilmene, Nienhaus, Marc, Killilea, Alison N., Betzig, Eric, Upadhyayula, Srigokul

Issue&Volume: 2024-10-17

Abstract: Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson–Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments. PetaKit5D offers versatile processing workflows for light sheet microscopy data including performant image input/output, geometric transformations, deconvolution and stitching. The software is efficient and scalable to petabyte-size datasets.

DOI: 10.1038/s41592-024-02475-4

Source: https://www.nature.com/articles/s41592-024-02475-4