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GPU 加速的射线投射法用于三维纤维方向分析。

GPU-accelerated ray-casting for 3D fiber orientation analysis.

机构信息

Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Institute for Automation and Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany.

出版信息

PLoS One. 2020 Jul 29;15(7):e0236420. doi: 10.1371/journal.pone.0236420. eCollection 2020.

Abstract

Orientation analysis of fibers is widely applied in the fields of medical, material and life sciences. The orientation information allows predicting properties and behavior of materials to validate and guide a fabrication process of materials with controlled fiber orientation. Meanwhile, development of detector systems for high-resolution non-invasive 3D imaging techniques led to a significant increase in the amount of generated data per a sample up to dozens of gigabytes. Though plenty of 3D orientation estimation algorithms were developed in recent years, neither of them can process large datasets in a reasonable amount of time. This fact complicates the further analysis and makes impossible fast feedback to adjust fabrication parameters. In this work, we present a new method for quantifying the 3D orientation of fibers. The GPU implementation of the proposed method surpasses another popular method for 3D orientation analysis regarding accuracy and speed. The validation of both methods was performed on a synthetic dataset with varying parameters of fibers. Moreover, the proposed method was applied to perform orientation analysis of scaffolds with different fibrous micro-architecture studied with the synchrotron μCT imaging setup. Each acquired dataset of size 600x600x450 voxels was analyzed in less 2 minutes using standard PC equipped with a single GPU.

摘要

纤维的取向分析广泛应用于医学、材料和生命科学领域。取向信息可用于预测材料的性能和行为,以验证和指导具有控制纤维取向的材料的制造过程。同时,高分辨率非侵入式 3D 成像技术的探测器系统的发展导致每个样本生成的数据量显著增加,高达数十千兆字节。尽管近年来开发了大量的 3D 取向估计算法,但它们都无法在合理的时间内处理大型数据集。这一事实使得进一步的分析变得复杂,并使得无法快速反馈来调整制造参数。在这项工作中,我们提出了一种新的方法来量化纤维的 3D 取向。与另一种流行的 3D 取向分析方法相比,所提出方法在准确性和速度方面都有优势。对具有不同纤维参数的合成数据集对两种方法进行了验证。此外,还将所提出的方法应用于使用同步加速器 μCT 成像装置研究的具有不同纤维微观结构的支架进行取向分析。使用配备单个 GPU 的标准 PC,每个大小为 600x600x450 体素的采集数据集在不到 2 分钟的时间内进行分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d51b/7390437/7ded5d8e1988/pone.0236420.g001.jpg

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