基于微ct扫描的骨小梁合成图像的生成

Inf. Comput. Pub Date : 2023-07-01 DOI:10.3390/info14070375
Jonas Grande-Barreto, Eduardo Polanco-Castro, H. Peregrina-Barreto, Eduardo Rosas-Mialma, Carmina Puig-Mar
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引用次数: 1

摘要

创建小梁组织的合成图像为研究人员提供了另一种方法来验证设计用于研究小梁骨的算法。开发合成图像需要基线数据,例如数字生物样本或模板的数据集,这些数据通常由于隐私限制而不可用。即使这个基线是可用的,标准过程也会将这些信息组合起来,以生成单个模板作为起点,从而减少生成的合成图像中的可变性。这项工作提出了一种方法来建立小梁骨结构的合成图像,创建一个3D网络来模拟它。接下来,模拟了微型ct扫描仪的技术特点、小梁骨的生物力学特性以及生成合成图像的成像过程的物理原理。所提出的方法不需要生物样本、数据集或模板来生成合成图像。由于构建的每个合成图像都是独一无二的,因此该方法能够生成大量的合成图像,有助于在不同成像条件下比较算法的性能。利用参考的微架构参数对合成图像进行了评估,实验结果表明,得到的值与需要初始数据的方法相匹配。这种方法的范围涵盖了与在进一步的生物医学研究中使用合成图像或开发教育培训工具以理解医学图像相关的研究方面。
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Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans
Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image.
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