LiveLattice:使用内存效率高的转换算法实现倾斜光片显微镜数据的实时可视化。

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Zichen Wang, Hiroyuki Hakozaki, Gillian McMahon, Marta Medina-Carbonero, Johannes Schöneberg
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引用次数: 0

摘要

光片荧光显微镜(LSFM)是一种著名的荧光显微镜技术,可为生物样品的四维(4D;x、y、z、时间)成像提供更高的时间分辨率。包括倒置选择性平面照明显微镜(iSPIM)和晶格光片显微镜(LLSM)在内的一些最新技术,可使样品基底相对于检测物镜的光轴成斜角移动。这种倾斜样品扫描 LSFM 的数据需要随后进行纠偏和旋转,以便进行适当的可视化和分析。目前,此类数据预处理操作需要分配大量内存,对大型 4D 数据集的计算提出了巨大挑战。因此,与数据采集时间相比,数据预处理时间更长,这就限制了在显微镜采集数据时实时查看数据的能力。为了在不需要大量硬件的情况下快速预处理大型光片显微镜数据集,我们开发了 WH-Transform,这是一种内存效率高的转换算法,用于对原始数据集进行纠偏和旋转。以传统方法和现有软件为基准,与其他方法的三次方和二次方运行时间相比,我们的方法显示了线性运行时间。在单个工作站上使用具有 24 GB 内存的 GPU,可在 3 秒内完成对 2 GB(512 × 1536 × 600 像素)原始三维体积的预处理。将我们的方法应用于人类肝细胞、肺类器官组织和脑类器官组织的 4D LLSM 数据集,可在数秒内完成快速准确的预处理。重要的是,这样的预处理速度现在可以实现显微镜原始数据流的实时可视化,大大提高了 LLSM 在生物学中的可用性。总之,这一进步为光片显微镜带来了变革性的潜力,使其能够在标准工作站上进行实时、即时的数据预处理、可视化和分析,从而彻底改变了 LLSM 和类似显微镜的生物成像应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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