基于机械波散射现象的a型超声光谱测量3d打印骨模型密度的初步研究

H. Susanti, Husneni Mukhtar, S. Suprijanto, Willy Anugrah Cahyadi
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引用次数: 0

摘要

在印度尼西亚,骨质疏松症的患病率很高。鉴于它可能给人民带来的经济负担,必须认真对待这一情况。双能x线吸收仪是诊断骨质疏松症(DEXA)的金标准。然而,由于其高成本、不可携带性和辐射风险,DEXA不能应用于大量人群。另一种评估骨质量的方法是超声。它更便宜、便携,而且没有辐射风险。在这项初步研究中,设计了用于评估3d打印骨模型密度的a型超声光谱原型。单元件换能器(Transmit-Tx/Receive-Rx)、基于fpga的可重构模块化超声波束形成系统和树莓派3是系统的控制单元。原始射频(RF)信号从3d打印骨模型的三种密度变化中获取,即100%,60%和40%,以表示正常骨,骨质减少和骨质疏松症。所设计的原型能够充分表征3d打印骨模型的机械波散射规律,即随着骨模型密度的增加,最大振幅呈增加趋势。延迟时间和功率谱密度(PSD)的趋势正好相反。这三个信号参数是表征骨密度的潜在候选参数。在以后的工作中,选择的候选参数可以作为参考值,同时添加重要的数据,以便使用机器学习方法提取骨密度水平的代表性特征,即正常骨、骨质减少和骨质疏松症。
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Initial Study of A-mode Ultrasound Spectroscopy Through Mechanical Wave Scattering Phenomenon for Measuring 3D-printed Bone Model Density
In Indonesia, the prevalence of osteoporosis is high. Given the economic burden it may impose on the population, this condition must be taken seriously. Dual-energy X-ray absorptiometry is the gold standard for diagnosing osteoporosis (DEXA). However, due to its high cost, non-portability, and radiation risk, DEXA cannot be applied to large populations. An alternative method for evaluating bone quality is ultrasound. It is more affordable, portable, and has no radiation risk. In this preliminary study, an A-mode ultrasound spectroscopy prototype for assessing the density of a 3D-printed bone model is designed. A single-element transducer (Transmit-Tx/Receive-Rx), a reconfigurable and modular FPGA-based ultrasound beamformer system, and a Raspberry Pi 3 are the system's control units. The raw radio frequency (RF) signal is acquired from three variations of density of the 3D-printed bone model, i.e., 100%, 60%, and 40%, to represent normal bone, osteopenia, and osteoporosis. The designed prototype can adequately characterize the mechanical wave scattering pattern of the 3D-printed bone model indicated by the increased tendency in the maximum amplitude when the density of the bone model is increasing. The tendency is the opposite for delay time and Power Spectral Density (PSD). These three signal parameters are potential candidate parameters to represent bone density. For future work, the selected candidate parameters can later be used as reference values while adding a significant data so that a machine learning method can be employed to extract representative features of bone density level, i.e., normal bone, osteopenia, and osteoporosis.
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来源期刊
International Journal of Mechanics
International Journal of Mechanics Engineering-Computational Mechanics
CiteScore
1.60
自引率
0.00%
发文量
17
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