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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李翔傑(Hsiang-Chieh Lee) | |
| dc.contributor.author | Teng-Chieh Chang | en |
| dc.contributor.author | 張登傑 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:15:29Z | - |
| dc.date.available | 2022-08-20 | |
| dc.date.copyright | 2020-09-17 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-18 | |
| dc.identifier.citation | [1] D. Huang et al., 'Optical coherence tomography,' Science, vol. 254, no. 5035, p. 1178, 1991. [2] A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, 'Measurement of intraocular distances by backscattering spectral interferometry,' Optics Communications, vol. 117, no. 1, pp. 43-48, 1995/05/15/ 1995. [3] S. H. Yun, G. J. Tearney, J. F. de Boer, N. Iftimia, and B. E. Bouma, 'High-speed optical frequency-domain imaging,' Optics Express, vol. 11, no. 22, pp. 2953-2963, 2003/11/03 2003. [4] S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, 'High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter,' Opt. Lett., vol. 28, no. 20, pp. 1981-1983, 2003/10/15 2003 [5] N. A. Nassif et al., 'In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve,' Optics Express, vol. 12, no. 3, pp. 367-376, 2004/02/09 2004. [6] W.-Y. Oh, B. J. Vakoc, M. Shishkov, G. J. Tearney, and B. E. Bouma, ' gt;400 kHz repetition rate wavelength-swept laser and application to high-speed optical frequency domain imaging,' Opt. Lett., vol. 35, no. 17, pp. 2919-2921, 2010/09/01 2010. [7] T. E. Ustun, N. V. Iftimia, R. D. Ferguson, and D. X. Hammer, 'Real-time processing for Fourier domain optical coherence tomography using a field programmable gate array,' Review of Scientific Instruments, vol. 79, no. 11, p. 114301, 2008. [8] D.-h. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, 'Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,' Biomedical optics express, vol. 3, no. 12, pp. 3067-3086, 2012. [9] A. E. Desjardins, B. J. Vakoc, M. J. Suter, S.-H. Yun, G. J. Tearney, and B. E. Bouma, 'Real-time FPGA processing for high-speed optical frequency domain imaging,' IEEE transactions on medical imaging, vol. 28, no. 9, pp. 1468-1472, 2009. [10] S. Van der Jeught, A. Bradu, and A. G. Podoleanu, 'Real-time resampling in Fourier domain optical coherence tomography using a graphics processing unit,' Journal of Biomedical Optics, vol. 15, no. 3, p. 030511, 2010. [11] Y. Watanabe and T. Itagaki, 'Real-time display on Fourier domain optical coherence tomography system using a graphics processing unit,' Journal of Biomedical Optics, vol. 14, no. 6, p. 060506, 2009. [12] J. U. Kang et al., 'Real-time three-dimensional Fourier-domain optical coherence tomography video image guided microsurgeries,' Journal of biomedical optics, vol. 17, no. 8, p. 081403, 2012. [13] M. Draelos, B. Keller, C. Viehland, O. M. Carrasco-Zevallos, A. Kuo, and J. Izatt, 'Real-time visualization and interaction with static and live optical coherence tomography volumes in immersive virtual reality,' Biomedical Optics Express, vol. 9, no. 6, pp. 2825-2843, 2018/06/01 2018. [14] B. Barney, 'Introduction to parallel computing,' Lawrence Livermore National Laboratory, vol. 6, no. 13, p. 10, 2010. [15] L. Dagum and R. Menon, 'OpenMP: An Industry-Standard API for Shared-Memory Programming,' IEEE Comput. Sci. Eng., vol. 5, no. 1, pp. 46–55, 1998. [16] B. Chapman, G. Jost, and R. Van Der Pas, Using OpenMP: portable shared memory parallel programming. MIT press, 2008. [17] I. Kuon, R. Tessier, and J. Rose, FPGA architecture: Survey and challenges. Now Publishers Inc, 2008. [18] NVIDIA, “NVIDIA CUDA C Programming Guide.” [19] J. A. Izatt, M. A. Choma, and A.-H. Dhalla, 'Theory of Optical Coherence Tomography,' in Optical Coherence Tomography: Technology and Applications, W. Drexler and J. G. Fujimoto Eds. Cham: Springer International Publishing, 2015, pp. 65-94. [20] R. Leitgeb, C. Hitzenberger, and A. F. Fercher, 'Performance of fourier domain vs. time domain optical coherence tomography,' Optics express, vol. 11, no. 8, pp. 889-894, 2003. [21] M. A. Choma, M. V. Sarunic, C. Yang, and J. A. Izatt, 'Sensitivity advantage of swept source and Fourier domain optical coherence tomography,' Optics Express, vol. 11, no. 18, pp. 2183-2189, 2003/09/08 2003. [22] AlazarTech, 'ATS-GPU', Retrieved from https://www.alazartech.com /product/ats-gpu?gclid=EAIaIQobChMI7YismKWf6wIV8Z7CCh0A0wAGE AAYASAAEgIAqPD_BwE [23] NVIDIA, “NVIDIA CUDA Profilor User Guide.” [24] Y. Huang, T. Tsai, T. Chen, C. Chueh, Y. Hung, and H. Lee, 'The Single Software Architecture Supporting Fourier Domain Optical Coherence Tomography System,' in 2019 IEEE International Conference on BioPhotonics (BioPhotonics), 15-18 Sept. 2019 2019, pp. 1-2, doi: 10.1109/BioPhotonics.2019.8896724. [25] Y. Jian, K. Wong, and M. Sarunic, 'GPU Accelerated OCT Processing at Megahertz Axial Scan rate and High Resolution Video Rate Volumetric Rendering,' Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 8571, 03/20 2013. [26] D. Ruijters, B. M. ter Haar Romeny, and P. Suetens, 'Efficient GPU-Based Texture Interpolation using Uniform B-Splines,' Journal of Graphics Tools, vol. 13, no. 4, pp. 61-69, 2008/01/01 2008. [27] D. Ruijters and P. Thévenaz, 'GPU prefilter for accurate cubic B-spline interpolation,' The Computer Journal, vol. 55, no. 1, pp. 15-20, 2012. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68239 | - |
| dc.description.abstract | 在本篇論文中，我們使用了圖形處理器(GPU)加速傅立葉式光學同調斷層掃描術(FD-OCT)的影像重建。首先、我們透過AlazarTech提供的應用程式介面編撰加速OCT影像處理的程式，並與我們實驗室所開發的使用者介面結合。透過將其與現有多執行緒的程式做影像重建的速度比較，發現使用GPU在影像處理的速度上有顯著的提升。再者，我們透過鑷子在牙齒樣本上的移動模擬手術的過程，即時影像的呈現相當流暢。即便AlazarTech提供很方便的開發工具，但針對需要波長校正的光源並沒有相對應的函式，且實驗室未來需對GPU上的資料作更進一步的處理。基於這些原因，我們開發了利用統一整合架構的影像處理方法，透過平行運算的方式加速OCT影像處理，並且用NVIDIA Visual Profiler對程式進行分析及優化，使其在影像處理的表現與AlazarTech所提供的函式是可媲美的。最後我們比較幾種常見的內插方式在計算效率以及影像品質的表現，選定三次樣條插值(Cubic spline interpolation)做為波長校正的方式。在未來的研究方向，可以利用目前所發展的程式對空間頻域式光學同調斷層掃描術(SD-OCT)進行影像的處理，甚至可以利用GPU進行三維OCT影像的視覺化或是進行組織分類。 | zh_TW |
| dc.description.abstract | In this thesis work, we have utilized the graphics processing unit (GPU) to accelerate the reconstruction of the Fourier-domain optical coherence tomography (FD-OCT) images. First of all, we utilized the application programming interface (API) functions provided by AlazarTech Inc. to develop a GPU-based OCT processing method. Then, we integrated this processing method with our in-house developed graphic user interface (GUI). To demonstrate the feasibility of our work, we compared the GPU-based processing method with our existing multithreading processing method. The result shows that the processing speed has been substantially enhanced. Moreover, we have demonstrated the imaging of moving a tweezer on the tooth specimen to simulate the surgery, and the instant preview has displayed smoothly without delay. Even though AlazarTech Inc. provided us a convenience development toolkit, the API functions do not support the resampling process, essential for the OCT system requiring wavelength calibration procedure. Besides, in the future, we aim to apply an additional imaging process algorithm to the reconstructed OCT images with GPU. Owing to these reasons, we would like to develop a GPU processing method based on Compute Unified Device Architecture (CUDA) to manage the data on GPU more efficiently. We used the NVIDIA Visual Profiler to analyze the efficiency of the developed program and tried to optimize its performance comparable to the computation performance with AlazarTech GPU APIs. Lastly, we compared three different interpolation methods with their image quality and computing efficiency, and we eventually decided to use cubic spline interpolation as the default wavelength calibration algorithm. In future work, we can acquire and perform real-time processing on the data acquired with spectral-domain OCT leveraging the framework developed in this thesis. Moreover, we can display the visualization of the three-dimensional (3D) OCT images or compute some massive calculations work, such as tissue segmentation, with the support of the GPU. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:15:29Z (GMT). No. of bitstreams: 1 U0001-1708202015364200.pdf: 1980444 bytes, checksum: 191f473c69280ba99586b3df3c041266 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 致謝 i 中文摘要 ii ABSTRACT iv LIST OF FIGURES vi LIST OF TABLE ix CONTENTS x Chapter 1. Introduction 1 1.1 Motivations 1 1.2 Parallel computing methods 4 1.2.1 Central processing units (CPU) with multithreading 5 1.2.2 Field programmable gate array (FPGA) 6 1.2.3 Digital signal processor (DSP) 6 1.2.4 Graphics processing unit (GPU) 6 1.3 Scope of the thesis 7 Chapter 2. Optical Coherence Tomography 9 2.1 Theory of optical coherence tomography: low coherence interferometry 9 2.2 Fourier-domain optical coherence tomography 13 2.2.1 Spectral-domain optical coherence tomography 14 2.2.2 Swept-source optical coherence tomography 15 2.3 Imaging resolution 16 2.3.1 Axial resolution 16 2.3.2 Lateral resolution 17 2.4 Sensitivity and sensitivity roll-off 18 2.5 Signal processing 19 2.5.1 Background subtraction 19 2.5.2 Wavelength calibration (resampling) 20 2.5.3 Dispersion compensation 21 Chapter 3. Parallel computing with graphic processing unit (GPU) 22 3.1 Introduction to GPU and GPU architecture 22 3.2 Compute Unified Device Architecture (CUDA) platform 25 3.3 AlazarTech GPU library 26 3.4 NVIDIA Visual Profiler 27 Chapter 4 Experiment Setup and Methods 29 4.1 System setup of the swept-source OCT 29 4.2 C++ graphic user interface (GUI) 31 4.3 GPU-accelerated C++ GUI for real-time OCT imaging 34 4.3.1 Framework I – AlazarTech GPU library 34 4.3.2 Framework II – in-house developed GPU processing algorithm 35 Chapter 5. Experimental Results and Discussion 40 5.1 Benchmark performance 40 5.2.1 Framework I – AlazarTech GPU library 41 5.2.2 Framework II – in-house developed GPU processing algorithm without wavelength calibration 45 5.2.3 Framework II – in-house developed GPU processing algorithm with wavelength calibration 47 5.2 Live preview and the live demo for ATS version 51 Chapter 6 Conclusion and future work 54 6.1 Conclusion 54 6.2 Future work 55 REFERENCE 56 | |
| dc.language.iso | en | |
| dc.subject | 光學同調斷層掃描術 | zh_TW |
| dc.subject | 圖形處理器 | zh_TW |
| dc.subject | 平行運算 | zh_TW |
| dc.subject | 統一計算架構 | zh_TW |
| dc.subject | 三維視覺化 | zh_TW |
| dc.subject | optical coherence tomography | en |
| dc.subject | graphics processing unit | en |
| dc.subject | parallel computing | en |
| dc.subject | Compute Unified Device Architecture (CUDA) | en |
| dc.subject | three-dimensional visualization | en |
| dc.title | 利用圖形處理器加速傅立葉式光學同調斷層掃描術之即時化影像處理 | zh_TW |
| dc.title | Graphics processing unit (GPU)-accelerated imaging engine with Fourier-domain optical coherence tomography framework for real-time imaging processing | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李正匡(Cheng-Kuang Lee),蔡孟燦(Meng-Tsan Tsai) | |
| dc.subject.keyword | 光學同調斷層掃描術,圖形處理器,平行運算,統一計算架構,三維視覺化, | zh_TW |
| dc.subject.keyword | optical coherence tomography,graphics processing unit,parallel computing,Compute Unified Device Architecture (CUDA),three-dimensional visualization, | en |
| dc.relation.page | 60 | |
| dc.identifier.doi | 10.6342/NTU202003772 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 光電工程學研究所 | zh_TW |
| 顯示於系所單位： | 光電工程學研究所 | |
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