Li Jian, Bloch Pavel, Xu Jing, Sarunic Marinko V, Shannon Lesley
School of Engineering Sciences, Simon Fraser University, V5A 1S6 Burnaby, BC, Canada.
Appl Opt. 2011 May 1;50(13):1832-8. doi: 10.1364/AO.50.001832.
Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.
与传统时域光学相干断层扫描（TD-OCT）相比，傅里叶域光学相干断层扫描（FD-OCT）为非侵入性活体生物医学成像提供了更快的线扫描速率、更高的分辨率和更高的灵敏度。然而，由于FD-OCT的信号处理计算量很大，实时FD-OCT应用需要强大的计算平台才能提供可接受的性能。图形处理单元（GPU）已被用作协处理器，通过利用其相对简单的编程模型来利用线程级并行性来加速FD-OCT。不幸的是，GPU并不与其主机处理器“共享”内存，这就需要在GPU和CPU之间进行额外的数据传输。在本文中，我们在消费级GPU/CPU平台上实现了一个完整的FD-OCT加速器。我们的数据采集系统采用基于光谱仪的检测和双臂干涉仪拓扑结构，并采用数值色散补偿进行视网膜成像。我们证明，由于GPU平台的内存模型，最大线扫描速率由内存传输时间而非处理时间决定。最后，我们讨论了基于GPU的加速器的性能趋势与FD-OCT数据速率的预期未来需求相比情况如何。
