Aguénounon Enagnon, Dadouche Foudil, Uhring Wilfried, Gioux Sylvain
University of Strasbourg, ICube Laboratory, 300 Boulevard Sébastien Brant, 67412 Illkirch, France.
Biomed Opt Express. 2019 Jul 11;10(8):3916-3928. doi: 10.1364/BOE.10.003916. eCollection 2019 Aug 1.
The development of real-time, wide-field and quantitative diffuse optical imaging methods is becoming increasingly popular for biological and medical applications. Recent developments introduced a novel approach for real-time multispectral acquisition in the spatial frequency domain using spatio-temporal modulation of light. Using this method, optical properties maps (absorption and reduced scattering) could be obtained for two wavelengths (665 nm and 860 nm). These maps, in turn, are used to deduce oxygen saturation levels in tissues. However, while the acquisition was performed in real-time, processing was performed post-acquisition and was not in real-time. In the present article, we present CPU and GPU processing implementations for this method with special emphasis on processing time. The obtained results show that the proposed custom direct method using a General Purpose Graphic Processing Unit (GPGPU) and C CUDA (Compute Unified Device Architecture) implementation enables 1.6 milliseconds processing time for a 1 Mega-pixel image with a maximum average error of 0.1% in extracting optical properties.
实时、宽视野和定量漫射光学成像方法的发展在生物和医学应用中越来越受欢迎。最近的进展引入了一种利用光的时空调制在空间频率域进行实时多光谱采集的新方法。使用这种方法，可以获得两个波长（665纳米和860纳米）的光学特性图（吸收和约化散射）。这些图进而用于推断组织中的氧饱和度水平。然而，虽然采集是实时进行的，但处理是在采集后进行的，并非实时处理。在本文中，我们介绍了该方法的CPU和GPU处理实现方式，特别强调了处理时间。所得结果表明，所提出的使用通用图形处理单元（GPGPU）和C CUDA（计算统一设备架构）实现的定制直接方法，对于100万像素的图像，在提取光学特性时能够实现1.6毫秒的处理时间，最大平均误差为0.1%。
