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首页> 外文会议>Optical Coherence tomography and coherence domain optical methods in biomedicine XX >Coarse-Grained and Fine-Grained Parallel Optimization for Real-Time En-Face OCT Imaging
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Coarse-Grained and Fine-Grained Parallel Optimization for Real-Time En-Face OCT Imaging

机译：实时面部OCT成像的粗粒和细粒并行优化

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This paper presents parallel optimizations in the en-face (C-scan) optical coherence tomography (OCT) display. Compared with the cross-sectional (B-scan) imagery, the production of en-face images is more computationally demanding, due to the increased size of the data handled by the digital signal processing (DSP) algorithms. A sequential implementation of the DSP leads to a limited number of real-time generated en-face images. There are OCT applications, where simultaneous production of large number of en-face images from multiple depths is required, such as real-time diagnostics and monitoring of surgery and ablation. In sequential computing, this requirement leads to a significant increase of the time to process the data and to generate the images. As a result, the processing time exceeds the acquisition time and the image generation is not in real-time. In these cases, not producing en-face images in real-time makes the OCT system ineffective. Parallel optimization of the DSP algorithms provides a solution to this problem. Coarse-grained central processing unit (CPU) based and finegrained graphics processing unit (GPU) based parallel implementations of the conventional Fourier domain (CFD) OCT method and the Master-Slave Interferometry (MSI) OCT method are studied. In the coarse-grained CPU implementation, each parallel thread processes the whole OCT frame and generates a single en-face image. The corresponding fine-grained GPU implementation launches one parallel thread for every data point from the OCT frame and thus achieves maximum parallelism. The performance and scalability of the CPU-based and GPU-based parallel approaches are analyzed and compared. The quality and the resolution of the images generated by the CFD method and the MSI method are also discussed and compared.
机译：本文介绍了正面（C扫描）光学相干断层扫描（OCT）显示器中的并行优化。与横截面（B扫描）图像相比，由于由数字信号处理（DSP）算法处理的数据量增加，因此，面对面图像的生成在计算上要求更高。 DSP的顺序实现导致有限数量的实时生成的面部图像。在OCT应用中，需要同时从多个深度生成大量的面部图像，例如实时诊断以及手术和消融的监测。在顺序计算中，此要求导致处理数据和生成图像的时间显着增加。结果，处理时间超过了获取时间，并且图像生成不是实时的。在这些情况下，由于无法实时生成面部图像，因此OCT系统无效。 DSP算法的并行优化为该问题提供了解决方案。研究了基于粗粒度中央处理器（CPU）和基于细粒度图形处理器（GPU）的常规傅里叶域（CFD）OCT方法和主从干涉（MSI）OCT方法的并行实现。在粗粒度CPU实现中，每个并行线程处理整个OCT帧并生成单个正面图像。相应的细粒度GPU实现为OCT帧中的每个数据点启动一个并行线程，从而实现了最大的并行度。分析和比较了基于CPU和基于GPU的并行方法的性能和可伸缩性。还讨论并比较了CFD方法和MSI方法生成的图像的质量和分辨率。

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