定量 OCT 提取衰减系数的原理、方法及临床应用

Significance The optical attenuation coefficient (AC), a fundamental tissue parameter, quantifies the rate at which light diminishes as it propagates through a medium. This parameter is crucial for the quantitative analysis of tissue properties using optical coherence tomography (OCT) signals. Pathological changes in biological tissues induce considerable alterations in their complex morphological structures and optical properties, which are influenced by factors such as tissue composition, architecture, and physiological conditions. OCT technology, which measures the intensity and temporal delay of faint coherent reflections and backscattered light, enables rapid, noncontact, and high-resolution in vivo imaging of tissues. Quantitative OCT (qOCT) combines OCT with advanced algorithms to extract tissue optical properties. This technique provides detailed morphological insights and quantitatively evaluates AC, providing highly precise information on tissue morphology, composition, and lesion detection. This review explores the principles of tissue AC extraction using qOCT (Fig. 2). The review also provides a comprehensive summary of the algorithms used to extract tissue AC values via qOCT, and examines the advantages, limitations, and applicable scenarios of the selected commonly used algorithms. Finally, the clinical applications of qOCT for extracting tissue AC values are discussed, along with the associated challenges and potential future directions for development. Progress The theoretical foundation of the algorithms used in qOCT primarily encompasses single scattering (SS) and multiple scattering (MS) models (Fig. 3). The SS model, which is suitable for weakly scattering samples or thin layers of densely scattering tissues, assumes a single backscattering event. Prominent SS-based algorithms include curve-fitting, fast frequency-domain, and depth-resolved methods, each with distinct strengths and limitations. In contrast, the MS model accounts for multiple scattering events, requiring a detailed analysis of the photon propagation pathways and probability distributions within tissues. Although these models are based on complex physical frameworks and computational methods that offer higher precision, they typically come with a tradeoff of slower processing speeds. Common MS-based algorithms include the Monte Carlo method, the extended Huygens‒Fresnel model, and the Maxwell’s equations‒based method. In addition, our research group has introduced an innovative approach called the multi-reference phantom-driven network. This approach employs multi-reference phantoms and deep learning techniques to implicitly model factors influencing OCT signal propagation, thereby enabling automated and accurate regression of the AC. The accuracy of AC extraction is influenced by various factors, including the detection systems, signal acquisition protocols, and processing methodologies (Fig. 4). Hardware parameters of the OCT detection system, such as the light source specifications, probe design, and system type, are critical for ensuring reliable calculation of the AC values. Preprocessing steps, including noise reduction, contrast enhancement, artifact removal, and motion correction, are essential for achieving accurate AC computations. Moreover, tissue heterogeneity, multilayer structures, and pathological changes, such as cell aggregation, neovascularization, or fibrosis, can complicate light‒tissue interactions and reduce the accuracy of AC calculations. qOCT offers high-resolution, quantitative insights into the optical properties of tissue and has demonstrated initial clinical applications in detecting ophthalmic, luminal, cancerous, superficial, and other diseases (Fig. 6). In ophthalmology, qOCT has become a vital tool for detecting and monitoring conditions such as glaucoma, macular degeneration, and diabetic retinopathy, enabling early intervention and improving patient outcomes (Fig. 7). In cancer diagnostics, qOCT can identify discernible changes in tissue morphology that result in notable alterations in AC and serve as a valuable biomarker for cancer monitoring and staging (Fig. 8). Moreover, qOCT has been increasingly recognized for its application in cardiovascular assessments, particularly in the detection and analysis of atherosclerotic plaques (Fig. 9). The ability of qOCT to precisely extract tissue AC values enables the quantitative assessment of lesions, offering robust support for the diagnosis and management of superficial conditions, such as skin lesions (Fig. 10). Additionally, qOCT holds great promise in the examination of lymphatic tissues, providing high-resolution images that reveal lymph nodes, vessels, blood vessels, and other microscopic structures, offering valuable insights into the structure and function of lymphatic tissues (Fig. 11). Conclusions and Prospects As an innovative quantitative analysis technique, qOCT enables the nondestructive, real-time acquisition of structural information and tissue AC in vivo, allowing for precise quantification of tissue structure and composition. The notable changes in tissue AC associated with pathological alterations provide robust clinical diagnostics using qOCT. Ongoing research focuses on developing depth-resolved, high-sensitivity, and high-resolution qOCT technologies for the real-time in vivo quantification of human tissues. These advancements aim to address existing challenges and broaden the clinical applications of this promising technology.