Institute of Biomedical Engineering & Technology, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Ultrasonics. 2024 Dec;144:107407. doi: 10.1016/j.ultras.2024.107407. Epub 2024 Jul 31.
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.
骨质疏松症是一种在老年人中发病率较高的系统性疾病，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势，可提供生物组织生理结构和化学信息的非侵入性和非放射性信息。由于骨组织的复杂结构特征，骨组织产生的 PA 信号是非平稳和非线性的。然而，传统的 PA 信号处理方法对于非平稳信号处理并不有效。在这项研究中，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），用于评估与骨健康密切相关的骨组织微观结构信息。通过对具有不同骨体积/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，分析不同固有模态函数（IMF）模式之间的相关性，并选择用于更高效 PA 时频分析的最佳模式。其次，使用多波长 HPSA 来评估骨组织化学成分的变化。结果表明，HPSA 方法可以自适应地高效区分具有不同 BV/TV 和微观结构条件的骨骼。它们进一步强调了 PA 技术在骨组织生物组织特征化方面的潜力，可用于早期和快速检测骨病。
