TomoPy

  • 文章类型: Journal Article
    同步加速器和中子计算机断层扫描数据的管理和处理可能是复杂的,劳动密集型和非结构化过程。用户投入大量时间来手动处理他们的数据(即组织数据/元数据,应用图像过滤器等。),并等待迭代对齐和重建算法的计算完成。在这项工作中,我们提出了解决这些问题的方法:TomoPyUI,用于众所周知的断层摄影数据处理包TomoPy的用户界面。这个高度可视化的Python软件包指导用户通过层析成像处理管道从数据导入,预处理,对齐,最后进行3D体积重建。TomoPyUI系统的中间数据和元数据存储系统改善了组织,以及检查和操作工具(在应用程序中内置)有助于避免工作流中断。值得注意的是,TomoPyUI完全在Jupyter环境中运行。在这里,我们提供了TomoPyUI的这些关键功能的摘要,以及层析成像处理管道的概述,讨论了现有层析成像处理软件的概况和TomoPyUI的目的,并演示了其在SSRL光束线6-2c上收集的真实层析成像数据的能力。
    The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applying image filters etc.) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI, a user interface for the well known tomography data processing package TomoPy. This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI, along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI, and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.
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  • 文章类型: Journal Article
    层析成像同步加速器数据的处理需要先进高效的软件才能在合理的时间内产生准确的结果。在本文中,两个软件工具箱的集成,TomoPy和ASTRA工具箱,which,一起,提供了一个强大的框架来处理层析成像数据,是presented。集成结合了两个工具箱的优点,例如TomoPy的用户友好性和CPU高效方法以及ASTRA工具箱的灵活性和基于GPU的优化重建方法。它表明,这两个工具箱可以很容易地安装和一起使用,只需要对现有的TomoPy脚本进行微小的更改。此外,结果表明,ASTRA工具箱的基于GPU的高效重建方法可以显着减少重建大型数据集所需的时间,与TomoPy的标准重建方法相比,先进的重建方法可以提高重建质量。
    The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy\'s standard reconstruction method.
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