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基于微焦点CT与数据约束模型的古代家猪牙齿显微结构研究
引用本文:张旭芳,王海鹏,李君,马杰,李建莉,杨玉双,侯亮亮,李莉.基于微焦点CT与数据约束模型的古代家猪牙齿显微结构研究[J].CT理论与应用研究,2020,29(6):677-685.
作者姓名:张旭芳  王海鹏  李君  马杰  李建莉  杨玉双  侯亮亮  李莉
作者单位:1. 山西大学 a)理论物理研究所;b)物理电子工程学院;c)历史文化学院;d)物理电子工程学院, 激光光谱研究所, 量子光学与光量子器件国家重点实验室, 太原 030006;
基金项目:国家自然科学基金(21206087)
摘    要:本文对一例考古发掘出土的古代家猪下颌骨及牙齿进行了微焦点CT成像,通过加置滤波片有效减弱了线束硬化对CT图像的影响。基于数据约束模型(DCM)对样品三维结构进行了定量计算,获取了包含小于CT图像体元尺寸在内的样品跨尺度结构信息。对样品中孔隙及高吸收组分的联通特性进行了跨尺度计算,得到这两种组分的三维团簇分布。与常见的图像阈值分割法相比,DCM模型有效减弱了部分体积效应对图像分析的影响。本文研究结果为考古样品的内部形态测量、结构分析提供了数据基础,为考古样品的数字建模存档提供一种新的方法。 

关 键 词:微焦点CT    数据约束模型    跨尺度结构
收稿时间:2020-06-30

Study on the Microstructure of Ancient Pig Teeth Based on Micro-CT and Data-constrained Modeling
Institution:1. a). Institute of Theoretical Physics and Department of Physics;b). College of Physics and Electronics Engineering;c). College of History and Culture;d). State Key Laboratory of Quantum Optics and Quantum Optic Devices, Institute of Laser Spectroscopy, College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China;2. CSIRO, Private Bag 10, Clayton, Victoria 3169, Australia
Abstract:In this paper,the jaw bone and teeth of an ancient pig unearthed from a historic site were imaged using a micro-CT.The three-dimensional(3D) structure of the sample was quantitatively calculated based on data-constrained modeling(DCM).The multi-scale structure of the sample including those smaller than CT imaging resolution size was obtained.The multi-scale connectivity of pore and high X-ray absorption compositions in the sample were calculated respectively.The 3D distribution form of pore and high X-ray absorption compositions clusters was obtained.Compared with common image threshold segmentation method,DCM efficiently reduces the influence of partial volume effect on image analysis.The results of this study provide a data basis for the internal morphological measurement and structural analysis of archaeological samples.The method presented in this paper can be applied to the digital modeling and archiving of archaeological samples. 
Keywords:
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