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FoamQuant:一个用于细胞材料时间分辨三维图像定量分析的Python软件包。

FoamQuant: a Python package for time-resolved 3D image quantification of cellular materials.

作者信息

Schott Florian, Dollet Benjamin, Santucci Stéphane, Raufaste Christophe, Mokso Rajmund

机构信息

Division of Solid Mechanics, LTH, Lund University, Lund, Sweden.

Université Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France.

出版信息

J Synchrotron Radiat. 2025 Sep 1;32(Pt 5):1370-1377. doi: 10.1107/S1600577525006629. Epub 2025 Aug 22.

DOI:10.1107/S1600577525006629
PMID:40844313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12416428/
Abstract

X-ray tomography is a well established technique for investigating three-dimensional bulk structures across scales, from macroscopic samples down to their microscopic constituents. The addition of a temporal dimension through dynamic, time-resolved acquisition results in four-dimensional datasets whose complexity often exceeds the processing capabilities of existing image analysis tools. To address the urgent need for a dedicated four-dimensional image analysis platform for cellular materials, we present FoamQuant-a free and open-source software package designed for batch processing and quantitative analysis of large time series of evolving cellular or foam-like materials. FoamQuant enables the extraction of key parameters such as liquid fraction (porosity), individual bubble (pore) volume and offers advanced characterization of mechanical properties, including elastic strain and stress fields as well as individual cell rearrangements. Its user-friendly, modular architecture is demonstrated through two case studies: (i) the orientation of plastic events in a flowing liquid foam, and (ii) bubble tracking in a coarsening albumin foam.

摘要

X射线断层扫描是一种成熟的技术,用于研究从宏观样本到微观成分的跨尺度三维整体结构。通过动态、时间分辨采集增加时间维度会产生四维数据集,其复杂性常常超出现有图像分析工具的处理能力。为满足对用于细胞材料的专用四维图像分析平台的迫切需求,我们展示了FoamQuant——一个免费的开源软件包,旨在对不断演变的细胞或泡沫状材料的大型时间序列进行批处理和定量分析。FoamQuant能够提取关键参数,如液体分数(孔隙率)、单个气泡(孔隙)体积,并提供力学性能的高级表征,包括弹性应变和应力场以及单个细胞重排。通过两个案例研究展示了其用户友好的模块化架构:(i)流动液体泡沫中塑性事件的取向,以及(ii)粗化白蛋白泡沫中的气泡跟踪。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/c8707aef9f6a/s-32-01370-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/324aba1aa46f/s-32-01370-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/432bba1ba8c6/s-32-01370-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/996315cd7b9f/s-32-01370-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/9912185696c9/s-32-01370-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/cc91f061e473/s-32-01370-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/c8707aef9f6a/s-32-01370-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/324aba1aa46f/s-32-01370-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/432bba1ba8c6/s-32-01370-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/996315cd7b9f/s-32-01370-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/9912185696c9/s-32-01370-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/cc91f061e473/s-32-01370-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6e8/12416428/c8707aef9f6a/s-32-01370-fig6.jpg

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