Ting Feng , Jieshu Li , Weiya Xie , Qian Cheng , Dean Ta
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引用次数: 0
Abstract
Osteoporosis is a systemic disease with a high incidence in the elderly and seriously affects the quality of life of patients. Photoacoustic (PA) technology, which combines the advantages of light and ultrasound, can provide information about the physiological structure and chemical information of biological tissues in a non-invasive and non-radiative way. Due to the complex structural characteristics of bone tissue, PA signals generated by bone tissue are non-stationary and nonlinear. However, conventional PA signal processing methods are not effective for non-stationary signal processing. In this study, an empirical mode decomposition (EMD)-based Hilbert-Huang transform (HHT) PA signal analysis method, called HHT PA signal analysis (HPSA), was developed to assess the microstructure information of bone tissue, which is closely related to bone health. The feasibility of the HPSA method in bone health assessment was proven by numerical simulation and experimental studies on animal samples with different bone volume/total volume (BV/TV) and bone mineral densities. First, based on adaptive EMD, the different modes correlated with multi-scale information were mined from the PA signal, the correlations between different intrinsic mode function (IMF) modes and BV/TVs were analyzed, and the optimal mode for more efficient PA time–frequency analysis was selected. Second, multi-wavelength HPSA was used to assess the changes in the chemical components of the bone tissue. The results demonstrate that the HPSA method can distinguish bones with different BV/TVs and microstructure conditions adaptively with high efficiency. They further emphasize the potential of PA techniques in characterizing biological tissues in bones for early and rapid detection of bone diseases.
骨质疏松症是一种全身性疾病，在老年人中发病率很高，严重影响患者的生活质量。光声（PA）技术结合了光和超声的优点，能以非侵入、非辐射的方式提供生物组织的生理结构和化学信息。由于骨组织复杂的结构特点，骨组织产生的 PA 信号是非稳态和非线性的。然而，传统的 PA 信号处理方法并不能有效地处理非稳态信号。本研究开发了一种基于经验模态分解（EMD）的希尔伯特-黄变换（HHT）PA 信号分析方法，称为 HHT PA 信号分析（HPSA），用于评估与骨骼健康密切相关的骨组织微观结构信息。通过对不同骨体积/总体积（BV/TV）和骨矿物质密度的动物样本进行数值模拟和实验研究，证明了 HPSA 方法在骨健康评估中的可行性。首先，基于自适应 EMD，从 PA 信号中挖掘出与多尺度信息相关的不同模式，分析了不同本征模函数（IMF）模式与 BV/TV 之间的相关性，并选择了最佳模式以进行更有效的 PA 时频分析。其次，利用多波长 HPSA 评估骨组织化学成分的变化。结果表明，HPSA 方法可以高效地自适应区分不同 BV/TV 和微结构条件的骨骼。这些结果进一步强调了 PA 技术在表征骨骼中的生物组织以早期快速检测骨骼疾病方面的潜力。
期刊介绍：
Ultrasonics is the only internationally established journal which covers the entire field of ultrasound research and technology and all its many applications. Ultrasonics contains a variety of sections to keep readers fully informed and up-to-date on the whole spectrum of research and development throughout the world. Ultrasonics publishes papers of exceptional quality and of relevance to both academia and industry. Manuscripts in which ultrasonics is a central issue and not simply an incidental tool or minor issue, are welcomed.
As well as top quality original research papers and review articles by world renowned experts, Ultrasonics also regularly features short communications, a calendar of forthcoming events and special issues dedicated to topical subjects.
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