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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81922完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李翔傑(Hsiang-Chieh Lee) | |
| dc.contributor.author | Yu-Ling Chen | en |
| dc.contributor.author | 陳育苓 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:06:42Z | - |
| dc.date.available | 2023-08-30 | |
| dc.date.copyright | 2021-10-21 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-06 | |
| dc.identifier.citation | [1] K. K. Lee, A. Mariampillai, X. Joe et al., “Real-time speckle variance swept-source optical coherence tomography using a graphics processing unit,” Biomedical optics express, 3(7), 1557-1564 (2012). [2] Y. Watanabe, Y. Takahashi, and H. Numazawa, “Graphics processing unit accelerated intensity-based optical coherence tomography angiography using differential frames with real-time motion correction,” Journal of biomedical optics, 19(2), 021105 (2013). [3] J. Xu, K. Wong, Y. Jian et al., “Real-time acquisition and display of flow contrast using speckle variance optical coherence tomography in a graphics processing unit,” Journal of biomedical optics, 19(2), 026001 (2014). [4] X. Wei, A. Camino, S. Pi et al., “Real-time cross-sectional and en face OCT angiography guiding high-quality scan acquisition,” Optics letters, 44(6), 1431-1434 (2019). [5] C. Chen, and V. X. Yang, “Gabor optical coherence tomographic angiography (GOCTA)(Part I): human retinal imaging in vivo,” Biomedical optics express, 8(12), 5724-5734 (2017). [6] C. Chen, W. Shi, J. Ramjist et al., “Gabor optical coherence tomographic angiography (GOCTA)(Part II): theoretical basis of sensitivity improvement and optimization for processing speed,” Biomedical optics express, 11(1), 227-239 (2020). [7] B. Barney, “Introduction to parallel computing,” Lawrence Livermore National Laboratory, 6(13), 10 (2010). [8] L. Dagum, and R. Menon, “OpenMP: an industry standard API for shared-memory programming,” IEEE computational science and engineering, 5(1), 46-55 (1998). [9] B. Chapman, G. Jost, and R. Van Der Pas, [Using OpenMP: portable shared memory parallel programming] MIT press, (2008). [10] NVIDIA, [CUDA C++ Programming Guide]. [11] I. Kuon, R. Tessier, and J. Rose, [FPGA architecture: Survey and challenges] Now Publishers Inc, (2008). [12] J. A. Izatt, and M. A. Choma, [Theory of optical coherence tomography] Springer, 47-72 (2008). [13] R. Leitgeb, C. Hitzenberger, and A. F. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Optics express, 11(8), 889-894 (2003). [14] M. A. Choma, M. V. Sarunic, C. Yang et al., “Sensitivity advantage of swept source and Fourier domain optical coherence tomography,” Optics express, 11(18), 2183-2189 (2003). [15] M. Guizar-Sicairos, S. T. Thurman, and J. R. Fienup, “Efficient subpixel image registration algorithms,” Optics letters, 33(2), 156-158 (2008). [16] M. Münter, M. Vom Endt, M. Pieper et al., “Dynamic contrast in scanning microscopic OCT,” Optics Letters, 45(17), 4766-4769 (2020). [17] H. M. Leung, M. L. Wang, H. Osman et al., “Imaging intracellular motion with dynamic micro-optical coherence tomography,” Biomedical Optics Express, 11(5), 2768-2778 (2020). [18] NVIDIA Corporation 2009, 'Optimizing CUDA.' [19] T. Mattson, and L. Meadows, “A “Hands-on” Introduction to OpenMP,” Intel Corporation, (2014). [20] NVIDIA, [NVIDIA CUDA Profiler User Guide]. [21] Y.-P. Huang, T.-Y. Tsai, T.-H. Chen et al., “A Generic Framework for Fourier-Domain Optical Coherence Tomography Imaging: Software Architecture and Hardware Implementations,” IEEE Access, 8, 191726-191736 (2020). [22] Y.-P. Huang, T.-Y. Tsai, T.-H. Chen et al., 'The Single Software Architecture Supporting Fourier Domain Optical Coherence Tomography System.' 1-2. [23] D. Ruijters, and P. Thévenaz, “GPU prefilter for accurate cubic B-spline interpolation,” The Computer Journal, 55(1), 15-20 (2012). [24] P. Heckbert, [Graphics gems IV (IBM version)] Elsevier, 474-485 (1994). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81922 | - |
| dc.description.abstract | "在本篇論文中，我們將圖形處理器(Graphics processing units, GPU)運用在光學同調斷層掃描術(Optical coherence tomography, OCT)中進行影像的即時處理與呈現。我們開發的這兩種演算法主要是對動態組織進行組織移動速度的分析，第一種為光學同調斷層掃描術血管造影(Optical coherence tomography angiography, OCTA)，此是基於我們實驗室現有以撰寫好的MATLAB版本進行改寫，使用C++語言並配合NVIDA CUDA toolkits所提供的函式庫進行編撰，透過GPU平行運算的方式達到即時呈現的效果；第二種則是動態光學同調斷層掃描術(Dynamic optical coherence tomography, D-OCT)，此為分析組織運動速度並在OCT影像上以對應的顏色呈現，讓使用者更能清楚分辨組織的結構與分層。而由於此兩種動態組織分析方法可能會受到活體樣本自主運動而造成錯誤分析結果，因此我們也用了亞像素影像配準(Subpixel image registration)的技術去對此進行校正。我們開發的這兩種演算法都已用過NVIDIA Visual Profiler對程式進行分析及優化，並且在手持式SD-OCT (Spectral-domain OCT)皮膚儀系統上進行實現與顯示，不論是在即時成像或三維影像視覺化的表現上都有相當優越的結果。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:06:42Z (GMT). No. of bitstreams: 1 U0001-2309202122314500.pdf: 3866565 bytes, checksum: 5fd5722922129aefcf969a7390e14569 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員審定書 i 誌 謝 ii 中文摘要 iii ABSTRACT iv 表目錄 vi 圖目錄 vii 第一章 介紹 1 1.1 動機 1 1.2 平行運算方法 4 1.2.1 多執行緒中心處理器 (Central Processing Units, CPU) 4 1.2.2 圖形處理器 (Graphics Processing Units, GPU) 5 1.2.3 Field Programmable Gate Array (FPGA) 5 1.3 論文範疇 7 第二章 光學同調斷層掃描術(Optical Coherence Tomography) 8 2.1 光學同調斷層掃描術原理：低同調干涉儀 8 2.2 傅立葉域光學同調斷層掃描術(Fourier-Domain Optical Coherence Tomography) 10 2.2.1 譜域光學同調斷層掃描術(Spectral-domain optical coherence tomography) 10 2.2.2 掃頻式光學同調斷層掃描術(Swept-source optical coherence tomography) 11 2.3 成像解析度 12 2.3.1 軸向解析度 12 2.3.2 橫向解析度 12 2.4 靈敏度與靈敏度滾降(Sensitivity and sensitivity roll-off) 14 2.5 動態OCT影像分析 15 2.5.1 影像配準(Image registration) 15 2.5.2 Optical coherence tomography angiography (OCTA)演算法 16 2.5.3 Dynamic optical coherence tomography (D-OCT)演算法 18 第三章 GPU平行運算 20 3.1 GPU介紹及GPU架構 20 3.2 Compute Unified Device Architecture (CUDA) 25 3.3 NVIDIA Visual Profiler 27 第四章 實驗方法與過程 28 4.1 OCT系統架構 28 4.2 C++ graphic user interface (GUI) 29 4.3 用於即時成像之GPU-accelerated C++ GUI及處理框架 31 4.3.1 先前開法之GPU-OCT processing algorithm 31 4.3.2 Framework I – GPU-OCTA processing algorithm 32 4.3.3 Framework II – GPU-D-OCT processing algorithm 35 第五章 實驗結果與討論 38 5.1 基準性能 38 5.1.1 Framework I – GPU-OCTA processing algorithm 38 5.1.2 Framework II – GPU-D-OCT processing algorithm 42 5.2 Developed GPU processing之即時成像表現與成像結果 46 5.2.1 GPU-OCTA processing之即時三維影像呈現 46 5.2.2 GPU-D-OCT processing之成像結果 47 第六章 結論與未來展望 48 6.1 結論 48 6.2 未來展望 48 參考文獻 49" | |
| dc.language.iso | zh-TW | |
| dc.subject | 影像配準 | zh_TW |
| dc.subject | 動態影像分析 | zh_TW |
| dc.subject | 光學同調斷層掃描術 | zh_TW |
| dc.subject | 三維視覺化 | zh_TW |
| dc.subject | 圖形處理器 | zh_TW |
| dc.subject | 血管造影 | zh_TW |
| dc.subject | 平行運算 | zh_TW |
| dc.subject | image registration | en |
| dc.subject | three-dimensional visualization | en |
| dc.subject | dynamic image analysis | en |
| dc.subject | optical coherence tomography angiography (OCTA) | en |
| dc.subject | parallel computing | en |
| dc.subject | graphics processing unit (GPU) | en |
| dc.subject | optical coherence tomography (OCT) | en |
| dc.title | 利用圖形處理器加速引擎於高速功能性光學同調斷層掃描術之開發 | zh_TW |
| dc.title | Development of High-speed Functional Optical Coherence Tomography (OCT) Imaging with a Graphic Processing Unit (GPU)-accelerated Engine | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡孟燦(Hsin-Tsai Liu),李正匡(Chih-Yang Tseng) | |
| dc.subject.keyword | 光學同調斷層掃描術,圖形處理器,平行運算,影像配準,血管造影,動態影像分析,三維視覺化, | zh_TW |
| dc.subject.keyword | optical coherence tomography (OCT),graphics processing unit (GPU),parallel computing,image registration,optical coherence tomography angiography (OCTA),dynamic image analysis,three-dimensional visualization, | en |
| dc.relation.page | 51 | |
| dc.identifier.doi | 10.6342/NTU202103330 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-08 | |
| 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 |
| dc.date.embargo-lift | 2023-08-30 | - |
| 顯示於系所單位： | 光電工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2309202122314500.pdf | 3.78 MB | Adobe PDF | 檢視/開啟 |
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