Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, United Kingdom.
Department of Biomedical Engineering, Duke University, Durham, NC, United States.
Neuroimage. 2024 Sep;298:120793. doi: 10.1016/j.neuroimage.2024.120793. Epub 2024 Aug 15.
Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.
漫反射相关光谱学（DCS）是评估深部组织微血管血液动力学的有力工具。近年来，传感器、激光和深度学习的进步进一步推动了新的 DCS 方法的发展。然而，对于初学者来说，不仅 DCS 理论框架已经非常复杂，而且组件选项和系统架构也非常广泛，这可能会让他们感到不知所措。为了方便新进入这个令人兴奋的领域，我们全面回顾了 DCS 硬件架构（连续波、频域和时域），并总结了相应的理论模型。此外，我们还讨论了高度集成的硅单光子雪崩二极管（SPAD）传感器在 DCS 中的新应用，比较了 SPAD 与现有传感器的区别，并回顾了其他组件（激光、传感器和相关器）以及数据分析工具，包括深度学习。讨论了在医学诊断中的潜在应用，并对未来的发展方向进行了展望，为开展 DCS 研究提供了有效的指导。
