Huang Guowu, Hu Yansen, Lin Weihao, Shen Chenfan, Yang Jianmin, Xie Zhineng, Ge Yifan, Jin Xin, Qian Xiafei, Xu Min
The Eighth Affiliated Hospital of Sun Yat-sen University, Department of Equipment, Shenzhen, China.
Wenzhou Medical University, Institute of Lasers and Biomedical Photonics, Biomedical Engineering College, Wenzhou, China.
J Biomed Opt. 2025 Apr;30(4):046008. doi: 10.1117/1.JBO.30.4.046008. Epub 2025 Apr 20.
Spatial frequency domain imaging (SFDI) is an emerging optical imaging modality for visualizing tissue absorption and scattering properties. This approach is promising for noninvasive wide field-of-view (FOV) monitoring of biophysiological processes .
We aim to develop deep-learning-enabled spatial frequency domain imaging (SFDI-net) for real-time large FOV imaging of the optical, structural, and physiological properties and demonstrate its application for probing the spatiotemporal dynamics of skin physiology.
SFDI-net, based on mapping of a two-layer structure into an equivalent homogeneous medium for spatially modulated light and with a convolutional neural network architecture, produces two-dimensional maps of optical, structural, and physiological parameters for bilayered tissue, including cutaneous hemoglobin concentration, oxygen saturation, scattering properties (reduced scattering coefficient and scattering power), melanin content, surface roughness, and epidermal thickness, with visible spatially modulated light at the camera frame rate.
Compared with traditional approaches, SFDI-net achieves a real-time inversion speed and significantly improves image quality by effectively suppressing noise while preserving tissue structure without oversmoothing. We demonstrate the application of the SFDI-net for monitoring the spatiotemporal dynamics of forearm skin physiology in reactive hyperemia and rhythmic respiration and reveal their intricate patterns in hemodynamics.
Deep-learning-enabled spatial frequency domain imaging and SFDI-net may offer insights into the cardiorespiratory system and have promising clinical utility for disease diagnosis, surveillance, and therapeutic assessment. Future hardware and software advancements will bring SFDI-net to clinical practice.
空间频域成像（SFDI）是一种新兴的光学成像方式，用于可视化组织的吸收和散射特性。这种方法有望用于生物生理过程的无创大视野（FOV）监测。
我们旨在开发基于深度学习的空间频域成像（SFDI-net），用于对光学、结构和生理特性进行实时大视野成像，并展示其在探测皮肤生理时空动态方面的应用。
SFDI-net基于将两层结构映射到空间调制光的等效均匀介质中，并采用卷积神经网络架构，以相机帧率的可见空间调制光生成双层组织的光学、结构和生理参数的二维图，包括皮肤血红蛋白浓度、血氧饱和度、散射特性（约化散射系数和散射功率）、黑色素含量、表面粗糙度和表皮厚度。
与传统方法相比，SFDI-net实现了实时反演速度，并通过有效抑制噪声同时保留组织结构而不过度平滑，显著提高了图像质量。我们展示了SFDI-net在监测反应性充血和节律性呼吸中前臂皮肤生理时空动态方面的应用，并揭示了其在血液动力学中的复杂模式。
基于深度学习的空间频域成像和SFDI-net可能为心肺系统提供见解，并在疾病诊断、监测和治疗评估方面具有广阔的临床应用前景。未来的硬件和软件进步将把SFDI-net带入临床实践。
