第一篇文章摘要学习Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system 基于GPU的定期非线性k频域OCT系统信号实时4D处理和可视化 写法：做成什么事情using什么硬件on什么系统 Kang Zhang* and Jin U. Kang Department of Electrical and Computer Engineering, The Johns Hopkins University, 3400 N. Charles Street, Baltimore 21218, Maryland, USA *kzhang8@jhu.edu Abstract: We realized graphics processing unit (GPU) based real-time 4D (3D + time) signal processing and visualization on a regular Fourier-domain optical coherence tomography (FD-OCT) system with a nonlinear k-space spectrometer. An ultra-high speed linear spline interpolation (LSI) method for λ-to-k spectral re-sampling is implemented in the GPU architecture, which gives average interpolation speeds of >3,000,000 line/s for 1024-pixel OCT (1024-OCT) and >1,400,000 line/s for 2048-pixel OCT (2048-OCT). The complete FD-OCT signal processing including λ-to-k spectral re-sampling, fast Fourier transform (FFT) and post-FFT processing have all been implemented on a GPU. The maximum complete A-scan processing speeds are investigated to be 680,000 line/s for 1024-OCT and 320,000 line/s for 2048-OCT, which correspond to 1GByte processing bandwidth. In our experiment, a 2048-pixel CMOS camera running up to 70 kHz is used as an acquisition device. Therefore the actual imaging speed is camera- limited to 128,000 line/s for 1024-OCT or 70,000 line/s for 2048-OCT. 3D Data sets are continuously acquired in real time at 1024-OCT mode, immediately processed and visualized as high as 10 volumes/second (12,500 A-scans/volume) by either en face slice extraction or ray-casting based volume rendering from 3D texture mapped in graphics memory. For standard FD-OCT systems, a GPU is the only additional hardware needed to realize this improvement and no optical modification is needed. This technique is highly cost-effective and can be easily integrated into most ultrahigh speed FD-OCT systems to overcome the 3D data processing and visualization bottlenecks. 术语提取和语句分析：We realized 什么硬件 based 做成什么事情 on a 什么系统 with 这个系统的特点，代表性参数. An 做成的事情的(代表性)一部分工作 for 这部分工作用的方法 is implemented in 什么硬件(换种表述词)，which gives 这部分工作达到的突出性能参数指标. The complete 文章工作的任务 including 主要方法/算法1，主要方法/算法2，主要方法/算法3(还可以往后列举) have all been implemented on a 什么硬件. The maximum 做到的性能最好的指标，which correspond to 硬件的一个指标. In our experiment，a 指标描述的相机工作在指标 is used as an acquisition device. Therefore the actual imaging speed is 相机-限制的 到 指标描述 for 模式1 and 模式2. 被处理的数据连续的 real time 采集在 模式1，immediately 处理和可视化 as 高/快/等等词语 as 指标 by either 方法1 or 方法2 in 硬件的哪里. For standard 什么系统，a 什么硬件 is the only additional hardware needed to 实现这个improvement and 没有需要光学的改变. This technique is 高度的 性价比 and can be easily integrated into 大多数的超高速 什么系统 to overcome the 什么问题/问题的瓶颈bottlenecks. 思路：总体句，说用什么硬件做成什么事，系统是什么有什么特点。这个方法其中一个代表性的工作用这个硬件可以实现。再说这部分方法达到的突出的性能指标描述。这里就把论文的工作和创新性做了个总概括。然后写论文完成的工作，由下面哪些方法/算法组成，列举几个，在什么硬件上实现了。方法的最大指标是好多好多，不同系统配置达到不同性能。.这里就可以展开点写论文的方法和性能。然后，如果有性能的限制，最好分析下限制来自于哪里。最后说，论文的工作的优点，这里是便宜，容易集成，性价比，再提一句，克服什么问题，困难，瓶颈。收工。 ?2010 Optical Society of America OCIS codes: (170.4500) Optical coherence tomography; (170.3890) Medical optics instrumentation; (200.4560) Optical data processing. http://blog.sciencenet.cn/blog-408189-338847.html |
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