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研究生:賴沛妤
研究生(外文):Pei-Yu Lai
論文名稱:多功能光學同調斷層術-在皮膚黑色素瘤小鼠中活體追蹤淋巴及血管影像
論文名稱(外文):Multi-functional optical coherence tomography for the in vivo longitudinal tracking of lymphangiography and angiography in a cutaneous melanoma mouse model
指導教授:郭文娟郭文娟引用關係
指導教授(外文):Wen-Chuan Kuo
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生醫光電研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:英文
論文頁數:60
中文關鍵詞:光學同調斷層術淋巴影像血管影像皮膚黑色素瘤
外文關鍵詞:optical coherence tomographylymphangiographyangiographycutaneous melanoma
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Contents
致謝 i
中文摘要 ii
Abstract iii
Contents iv
List of Figures vi
List of Tables xii
Chapter 1: Introduction - 1 -
1-1 Overview - 1 -
1-2 Motivation - 6 -
1-3 Thesis Structure - 7 -
Chapter 2: Optical Coherence Tomography - 8 -
2-1 Principle of OCT - 8 -
2-1-1 Time domain OCT - 11 -
2-1-2 Fourier domain OCT - 11 -
2-2 OCT Parameters - 13 -
2-2-1 Resolution - 13 -
2-2-2 Depth - 13 -
2-2-3 Sensitivity - 14 -
2-3 Functional OCT - 14 -
Chapter 3: Experimental Method - 16 -
3-1 MFOCT System - 16 -
3-1-1 System setup - 16 -
3-1-2 Scanning protocol and image acquisition process - 17 -
3-2 Animal Model and Experimental Procedure - 18 -
3-3 Image Processing Method - 20 -
3-3-1 Mean spectrum subtraction - 20 -
3-3-2 Dispersion compensation - 22 -
2-3-3 Region of interest (ROI) segmentation - 24 -
3-3-3 Angiography - 25 -
3-3-4 Lymphangiography - 28 -
3-4 Quantitative Analysis Method - 31 -
Chapter 4: Results - 33 -
4-1 MicroPET Images - 33 -
4-2 Microstructural Change - 34 -
4-3 Lymphangiogenesis and Angiogenesis - 39 -
4-4 Amelanotic Melanoma - 50 -
Chapter 5: Discussion - 53 -
Chapter 6: Conclusions and Prospects - 55 -
Reference - 57 -

List of Figures
Figure 1-1 Schematic of lymphatic circulation. [Image adapted from website: https://www.pinterest.co.uk/pin/376121006349908479/]. - 2 -
Figure 1-2 PET-CT after radical prostatectomy. CT images are in (D), PET images 60 min after the administration of 18F-choline are in (E) and merged PET-CT images are in (F). The high intensity spot in the PET scan is a small right inguinal lymph node with likely metastasis (F: transverse plane; L: saggital plane; A: coronal plane)[10] - 3 -
Figure 1-3 Coronal 3D MR lymphography image of the lower extremities obtained after subcutaneous injection of contrast material. Abnormal, dilated lymphatic vessels extend from the left calf to the inner thigh (small arrows). Some lymphatic vessels in the contralateral normal limb appear discontinuous (small arrows). Veins appear as linear structures with lower intensity (large arrows).[11] - 4 -
Figure 1-4 Near infrared imaging of healthy lymphatics in normal subjects. Lymphatic vessels in (G) hand, (H) arm, (I) foot, ankle, and leg, and (J) lower legs. Black spots are covered injection sites.[12] - 4 -
Figure 1-5 (b) OCT lymphangiography (c)cutaneous injection of Evan’s blue dye imaged by wide-field trans-illumination with a CCD camera. [16]…………..- 5 -
Figure 2-1 Schematic of a Michelson interferometer. - 9 -
Figure 2-2 The interference pattern shows up when the optical path length of the reference mirror matches one of the reflecting structures in the sample……..- 11 -
Figure 3-1 Schematic of MFOCT system. - 16 -
Figure 3-2 Output waveform for the (a)X-galvo, (b) Y-galvo and (c) data acquisition trigger - 18 -
Figure 3-3 Schematic of the experimental timeline. - 19 -
Figure 3-4 Photograph of the mouse ear. The scanned area was highlighted with white circles. - 20 -
Figure 3-5 MFOCT images processed (a) with and (b) without mean-spectrum subtraction. - 22 -
Figure 3-6 MFOCT images processed (a) with and (b) without dispersion compensation. - 24 -
Figure 3-7 Protocol for finding the upper boundary in ROI. - 24 -
Figure 3-8 MFOCT image with selected ROI (outlined in yellow). - 25 -
Figure 3-9 (A) The magnitude and absolute real part of the cross-correlation. (B) Example voxels are showing axial and lateral global fluctuations in the phase of OCT signals.[40] - 26 -
Figure 3-10 (a) Original MFOCT angiography and (b) MFOCT angiography processed by suppressing Fourier components on the vertical coordinate axes. - 28 -
Figure 3-11 The scattering signal along one A-scan within an MFOCT image of a mouse ear. - 28 -
Figure 3-12 MFOCT images processed (a) with and (b) without histogram equalization. - 29 -
Figure 3-13 MFOCT images processed (a) with and (b) without a bandpass filter. - 30 -
Figure 3-14 (a) Minimum intensity projection of MFOCT image, the selection was outlined in yellow, (b) segmented lymphatic vessel corresponding to the selection in (a) and (c) MFOCT lymphangiography. - 31 -
Figure 3-15 Flow chart of the image processing method. - 32 -
Figure 4-1 (a) Longitudinal 18 F-FDG MicroPET/CT images of 4-HT induced mouse and (b) Details of the square regions in the corresponding images in (a). The white arrow points to the cervical lymph node. - 33 -
Figure 4-2 Mean uptake values in cervical lymph nodes relative to leg versus time (N=3). Statistical significance was evaluated with Student t-test; *P < 0.05; N: number of mice. - 34 -
Figure 4-3 (a) Histologic sections and (b) corresponding MFOCT cross sectional images from 4-HT induced mouse ear at week 0, week 3, week 4 and week 6.(Scale bar = 1 mm) - 35 -
Figure 4-4 MFOCT cross-sectional image from (a) BRAFV600E control mice, and (b) 4-HT induced group. MFOCT 3D reconstruction image of (c) BRAFV600E control mice and 4-HT induced (d) mouse ear. - 35 -
Figure 4-5 (a) MFOCT en-face image and (b) corresponding photograph of the 4-HT induced mouse ear. - 36 -
Figure 4-6 (a) Photograph, and (b) the corresponding thickness map of 4-HT induced mouse ear at week 1, week 3 and week 4. White arrow on the photograph points to melanoma on the skin surface. - 37 -
Figure 4-7 Measured ear thickness at the 7 time points. Values are means ± standard deviation. Each time point was normalized to the initial value (week 0); N represents number of scanned areas. - 37 -
Figure 4-8 (a)Photograph and (b) corresponding thickness map of a 4-HT mouse ear. (Scale bar = 1mm) - 38 -
Figure 4-9 Measured ear thickness at the 4 time points. Values are means ± standard deviation. Each time point was normalized to the initial value (week 3). Statistical significance was evaluated with Student t-test; *P < 0.05; N represents number of scanned areas. - 38 -
Figure 4-10 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT lymphangiography, (c) MFOCT angiography and (d) MFOCT angiography merged with MFOCT lymphangiography. (Scale bar = 1mm) - 40 -
Figure 4-11 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT lymphangiography and (c) MFOCT angiography merged with MFOCT lymphangiography. (Scale bar = 1mm) - 41 -
Figure 4-12 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT angiography and (c) MFOCT lymphangiography. (Scale bar = 1mm) - 42 -
Figure 4-13 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT angiography and (c) MFOCT lymphangiography. (Scale bar = 1mm) - 42 -
Figure 4-14 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT angiography and (c) MFOCT lymphangiography. (Scale bar = 1mm) - 44 -
Figure 4-15 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT angiography and (c) MFOCT lymphangiography. (Scale bar = 1mm) - 45 -
Figure 4-16 Monitoring the angiography and lymphangiography within the BRAFV600E mouse ear. (a) photograph, (b) MFOCT angiography merged with MFOCT lymphangiography. (Scale bar = 1mm) - 47 -
Figure 4-17 Monitoring the angiography and lymphangiography within the 4-HT induced mouse ear. (a) photograph, (b) MFOCT angiography, (c) MFOCT lymphangiography and (d) MFOCT angiography merged with MFOCT lymphangiography. (Scale bar = 1mm) - 47 -
Figure 4-18 Quantitative measurement of (a) lymphatic and (b) blood vessel area density. Values are means ± standard deviation. Each time point was normalized to the initial value (week 0). Statistical significance was evaluated with Student t-test; *P < 0.05; **P<0.01. N represents number of scanned areas. - 48 -
Figure 4-19 Quantitative measurement of (a) lymphatic and (b) blood vessel area diameter. Values are means ± standard deviation. Each time point was normalized to the initial value (week 0). Statistical significance was evaluated with Student t-test; *P < 0.05; **P<0.01. N represents number of scanned areas. - 49 -
Figure 4-20 Quantitative measurement of (a) lymphatic and (b) blood vessel area tortuosity. Values are means ± standard deviation. Each time point was normalized to the initial value (week 0). Statistical significance was evaluated with Student t-test; *P < 0.05; **P<0.01. N represents number of scanned areas. - 49 -
Figure 4-21 Quantitative measurement of (a) lymphatic vessel density and (b) diameter. Values are means ± standard deviation. Each time point was normalized to the initial value (week 0). Statistical significance was evaluated with Student t-test; *P < 0.05; **P<0.01. N represents number of scanned areas. - 50 -
Figure 4-22 Monitoring the structural and vascular change within the 4-HT induced mouse ear. (a)Photograph, (b)MFOCT angiography merged with MFOCT lymphangiography, (c) density map of lymphatic vessel (d) diameter map of lymphatic vessel, (e) density map of blood vessel, (f) tortuosity map of blood vessel, and (g) thickness map of the 4-HT induced mouse ear. (Scale bar = 1mm)……………- 52 -
Figure 5-1 (a) Photograph, (b) MFOCT angiography and (c) MFOCT lymphangiography (d) MFOCT angiography merged with lymphangiography of the human arm………………………….- 56 -

List of Tables
Table 3-1 Specifications of the spectrometer - 17 -
1. Randolph, G.J., et al., The Lymphatic System: Integral Roles in Immunity. Annual review of immunology, 2017. 35: p. 31-52.
2. Kim, K.-W. and J.-H. Song, Emerging Roles of Lymphatic Vasculature in Immunity. Immune Network, 2017. 17(1): p. 68-76.
3. Cueni, L.N. and M. Detmar, The Lymphatic System in Health and Disease. Lymphatic research and biology, 2008. 6(3-4): p. 109-122.
4. Paduch, R., The role of lymphangiogenesis and angiogenesis in tumor metastasis. Cell Oncol (Dordr), 2016. 39(5): p. 397-410.
5. Lim, H.Y., et al., Hypercholesterolemic mice exhibit lymphatic vessel dysfunction and degeneration. Am J Pathol, 2009. 175(3): p. 1328-37.
6. Ran, S., et al., Lymphangiogenesis and Lymphatic Metastasis in Breast Cancer. Pathophysiology : the official journal of the International Society for Pathophysiology / ISP, 2010. 17(4): p. 229-251.
7. Mumprecht, V. and M. Detmar, Lymphangiogenesis and cancer metastasis. Journal of Cellular and Molecular Medicine, 2009. 13(8a): p. 1405-1416.
8. Munn, L.L. and T.P. Padera, Imaging the lymphatic system. Microvasc Res, 2014. 96: p. 55-63.
9. Eklund, L., M. Bry, and K. Alitalo, Mouse models for studying angiogenesis and lymphangiogenesis in cancer. Mol Oncol, 2013. 7(2): p. 259-82.
10. Fortuin, A., et al., Molecular and Functional Imaging for Detection of Lymph Node Metastases in Prostate Cancer. International Journal of Molecular Sciences, 2013. 14(7): p. 13842-13857.
11. Lu, Q., et al., MR Lymphography of Lymphatic Vessels in Lower Extremity with Gynecologic Oncology-Related Lymphedema. PLOS ONE, 2012. 7(11): p. e50319.
12. Rasmussen, J.C., et al., Lymphatic Imaging in Humans with Near-Infrared Fluorescence. Current opinion in biotechnology, 2009. 20(1): p. 74-82.
13. Takenaka, T., et al., Prediction of true-negative lymph node metastasis in clinical IA non-small cell lung cancer by measuring standardized uptake values on positron emission tomography. Surg Today, 2012. 42(10): p. 934-9.
14. Seo, M.J., et al., Detection of internal mammary lymph node metastasis with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography in patients with stage III breast cancer. Eur J Nucl Med Mol Imaging, 2014. 41(3): p. 438-45.
15. Gashev, A.A., T. Nagai, and E.A. Bridenbaugh, Indocyanine green and lymphatic imaging: current problems. Lymphat Res Biol, 2010. 8(2): p. 127-30.
16. Vakoc, B.J., et al., Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat Med, 2009. 15(10): p. 1219-23.
17. Qin, W., U. Baran, and R. Wang, Lymphatic response to depilation-induced inflammation in mouse ear assessed with label-free optical lymphangiography. Lasers Surg Med, 2015. 47(8): p. 669-76.
18. Yousefi, S., Z. Zhi, and R.K. Wang, Label-free optical imaging of lymphatic vessels within tissue beds in vivo. IEEE J Sel Top Quantum Electron, 2014. 20(2): p. 6800510.
19. Leachman, S.A., et al., Methods of Melanoma Detection. Cancer Treat Res, 2016. 167: p. 51-105.
20. Cadili, A. and K. Dabbs, Predictors of sentinel lymph node metastasis in melanoma. Can J Surg, 2010. 53(1): p. 32-6.
21. Paek, S.C., et al., The impact of factors beyond Breslow depth on predicting sentinel lymph node positivity in melanoma. Cancer, 2007. 109(1): p. 100-8.
22. McMasters, K.M., et al., Factors that predict the presence of sentinel lymph node metastasis in patients with melanoma. Surgery, 2001. 130(2): p. 151-6.
23. Morton, D.L., et al., Lymphatic mapping and sentinel lymphadenectomy for early-stage melanoma: therapeutic utility and implications of nodal microanatomy and molecular staging for improving the accuracy of detection of nodal micrometastases. Ann Surg, 2003. 238(4): p. 538-49; discussion 549-50.
24. Streit, M. and M. Detmar, Angiogenesis, lymphangiogenesis and melanoma metastasis. Oncogene, 2003. 22(20): p. 3172-3179.
25. Doeden, K., et al., Lymphatic invasion in cutaneous melanoma is associated with sentinel lymph node metastasis. J Cutan Pathol, 2009. 36(7): p. 772-80.
26. Storr, S.J., et al., Objective assessment of blood and lymphatic vessel invasion and association with macrophage infiltration in cutaneous melanoma. Mod Pathol, 2012. 25(4): p. 493-504.
27. Dadras, S.S., et al., Tumor Lymphangiogenesis. The American Journal of Pathology, 2003. 162(6): p. 1951-1960.
28. Dadras, S.S., et al., Tumor lymphangiogenesis predicts melanoma metastasis to sentinel lymph nodes. Mod Pathol, 2005. 18(9): p. 1232-42.
29. Shayan, R., et al., Lymphatic vessel density in primary melanomas predicts sentinel lymph node status and risk of metastasis. Histopathology, 2012. 61(4): p. 702-10.
30. Emmett, M.S., et al., Prediction of melanoma metastasis by the Shields index based on lymphatic vessel density. BMC Cancer, 2010. 10(1): p. 208.
31. Massi, D., et al., Tumour lymphangiogenesis is a possible predictor of sentinel lymph node status in cutaneous melanoma: a case-control study. J Clin Pathol, 2006. 59(2): p. 166-73.
32. Moraes Pinto Blumetti, T.C., et al., Optical coherence tomography (OCT) features of nevi and melanomas and their association with intraepidermal or dermal involvement: A pilot study. J Am Acad Dermatol, 2015. 73(2): p. 315-7.
33. Gambichler, T., et al., Characterization of benign and malignant melanocytic skin lesions using optical coherence tomography in vivo. J Am Acad Dermatol, 2007. 57(4): p. 629-37.
34. Huang, D., et al., Optical Coherence Tomography. Science (New York, N.Y.), 1991. 254(5035): p. 1178-1181.
35. Wojtkowski, M., et al., In vivo human retinal imaging by Fourier domain optical coherence tomography. J Biomed Opt, 2002. 7(3): p. 457-63.
36. Fercher, A.F., et al., Optical coherence tomography - principles and applications. Reports on Progress in Physics, 2003. 66(2): p. 239-303.
37. Srinivasan, V., A. C. Chan, and E. Lam, Doppler OCT and OCT Angiography for In Vivo Imaging of Vascular Physiology. 2012.
38. Dankort, D., et al., Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat Genet, 2009. 41(5): p. 544-52.
39. Wojtkowski, M., et al., Ultrahigh-resolution, high-speed, Fourier domain optical coherence tomography and methods for dispersion compensation. Optics Express, 2004. 12(11): p. 2404-2422.
40. Lee, J., et al., Motion correction for phase-resolved dynamic optical coherence tomography imaging of rodent cerebral cortex. Opt Express, 2011. 19(22): p. 21258-70.
41. Wang, R.K., Optical Microangiography: A Label Free 3D Imaging Technology to Visualize and Quantify Blood Circulations within Tissue Beds in vivo. IEEE J Sel Top Quantum Electron, 2010. 16(3): p. 545-54.
42. Chen, P.H., et al., Combination of structural and vascular optical coherence tomography for differentiating oral lesions of mice in different carcinogenesis stages. Biomed Opt Express, 2018. 9(4): p. 1461-1476.
43. Baran, U., et al., OCT-based label-free in vivo lymphangiography within human skin and areola. Sci Rep, 2016. 6: p. 21122.
44. Mumprecht, V., F. Roudnicky, and M. Detmar, Inflammation-induced lymph node lymphangiogenesis is reversible. Am J Pathol, 2012. 180(3): p. 874-9.
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