Massively parallel data processing for quantitative total flow imaging with optical coherence microscopy and tomography.

We present an application of massively parallel processing of quantitative flow measurements data acquired using spectral optical coherence microscopy (SOCM). The need for massive signal processing of these particular datasets has been a major hurdle for many applications based on SOCM. In view of this difficulty, we implemented and adapted quantitative total flow estimation algorithms on graphics processing units (GPU) and achieved a 150 fold reduction in processing time when compared to a former CPU implementation. As SOCM constitutes the microscopy counterpart to spectral optical coherence tomography (SOCT), the developed processing procedure can be applied to both imaging modalities. We present the developed DLL library integrated in MATLAB (with an example) and have included the source code for adaptations and future improvements.

本研究展示了利用光谱光相干显微镜（Spectral Optical Coherence Microscopy，简称SOCM）获取的定量流量测量数据的海量并行处理应用。针对此类数据集进行海量信号处理的需求，已成为基于SOCM的众多应用的主要障碍。鉴于这一难题，我们于图形处理单元（Graphics Processing Unit，简称GPU）上实施并适配了定量总流量估算算法，与之前的CPU实现相比，处理时间实现了150倍的缩减。鉴于SOCM是光谱光相干断层扫描（Spectral Optical Coherence Tomography，简称SOCT）在显微镜领域的对应技术，所开发的处理程序可适用于两种成像方式。我们展示了集成的DLL库在MATLAB中的应用实例（附带示例），并提供了源代码以供适配和未来的改进。