The development of real-time, wide-field and quantitative diffuse optical imaging methods to visualize functional and structural biomarkers of living tissues is a pressing need for numerous clinical applications including image-guided surgery. In this context, Spatial Frequency Domain Imaging (SFDI) is an attractive method allowing for the fast estimation of optical properties using the Single Snapshot of Optical Properties (SSOP) approach. Herein, we present a novel implementation of SSOP based on a combination of deep learning network at the filtering stage and Graphics Processing Units (GPU) capable of simultaneous high visual quality image reconstruction, surface profile correction and accurate optical property (OP) extraction in real-time across large fields of view. In the most optimal implementation, the presented methodology demonstrates megapixel profile-corrected OP imaging with results comparable to that of profile-corrected SFDI, with a processing time of 18 ms and errors relative to SFDI method less than 10% in both profilometry and profile-corrected OPs. This novel processing framework lays the foundation for real-time multispectral quantitative diffuse optical imaging for surgical guidance and healthcare applications. All code and data used for this work is publicly available at www.healthphotonics.org under the resources tab.

中文翻译：

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使用深度学习进行轮廓校正的光学特性的实时，宽视场和高质量单快照成像

实时，宽视场和定量扩散光学成像方法的发展，以可视化活组织的功能和结构生物标志物是对包括图像引导手术在内的众多临床应用的迫切需求。在这种情况下，空间频域成像（SFDI）是一种有吸引力的方法，可以使用光学特性的单快照（SSOP）方法快速估计光学特性。本文中，我们提出了一种基于过滤的深度学习网络和图形处理单元（GPU）相结合的SSOP的新颖实现，该图形处理单元能够同时实时地实现高视觉质量的图像重建，表面轮廓校正和精确的光学特性（OP）提取时间跨大视野。在最佳实施中 提出的方法论证明了百万像素轮廓校正的OP成像，其结果可与轮廓校正的SFDI媲美，处理时间为18 ms，相对于SFDI方法的误差在轮廓测定法和轮廓校正的OP中均小于10％。这种新颖的处理框架为手术指导和医疗保健应用实时多光谱定量漫射光学成像奠定了基础。用于此工作的所有代码和数据可在www.healthphotonics.org的“资源”选项卡下公开获得。这种新颖的处理框架为用于手术指导和医疗保健应用的实时多光谱定量漫射光学成像奠定了基础。用于此工作的所有代码和数据可在www.healthphotonics.org的“资源”选项卡下公开获得。这种新颖的处理框架为用于手术指导和医疗保健应用的实时多光谱定量漫射光学成像奠定了基础。用于此工作的所有代码和数据可在www.healthphotonics.org的“资源”选项卡下公开获得。