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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曾雪峰 | zh_TW |
dc.contributor.advisor | Snow H. Tseng | en |
dc.contributor.author | 吳天予 | zh_TW |
dc.contributor.author | Tien-Yu Wu | en |
dc.date.accessioned | 2023-08-01T16:19:38Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-08-01 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-04 | - |
dc.identifier.citation | X. Liu, Q. Liao, and H. Wang, "In vivo x-ray luminescence tomographic imaging with single-view data," Optics letters, vol. 38, no. 22, pp. 4530-4533, 2013.
X. Liu, H. Wang, and Z. Yan, "Nanobiomaterials in X-ray luminescence computed tomography (XLCT) imaging," in Nanobiomaterials in Medical Imaging: Elsevier, 2016, pp. 403-420. M. Bigas, E. Cabruja, J. Forest, and J. Salvi, "Review of CMOS image sensors," Microelectronics journal, vol. 37, no. 5, pp. 433-451, 2006. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, vol. 521, no. 7553, pp. 436-444, 2015. 張立昇, "基於深度學習之電腦斷層影像去雜訊," 碩士, 生物醫學影像暨放射 科 學 系 , 國 立 陽 明 大 學 , 新竹市 , 2020. S. A. Kassam, Signal detection in non-Gaussian noise. Springer Science & Business Media, 2012. T. Le, R. Chartrand, and T. J. Asaki, "A variational approach to reconstructing images corrupted by Poisson noise," Journal of mathematical imaging and vision, vol. 27, no. 3, pp. 257-263, 2007. A. H. Compton, X-Rays and electrons: An outline of recent X-Ray theory. D. Van Nostrand Company, 1926. D. Chen et al., "Cone beam x‐ray luminescence computed tomography: A feasibility study," Medical physics, vol. 40, no. 3, p. 031111, 2013. G. Pratx, C. M. Carpenter, C. Sun, and L. Xing, "X-ray luminescence computed tomography via selective excitation: a feasibility study," IEEE transactions on medical imaging, vol. 29, no. 12, pp. 1992-1999, 2010. C. Li, A. Martínez-Dávalos, and S. R. Cherry, "Numerical simulation of x-ray luminescence optical tomography for small-animal imaging," Journal of biomedical optics, vol. 19, no. 4, pp. 046002-046002, 2014. X. Liu, Q. Liao, and H. Wang, "Fast x-ray luminescence computed tomography imaging," IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1621-1627, 2013. D. Chen et al., "Quantitative cone beam X-ray luminescence tomography/X-ray computed tomography imaging," Applied Physics Letters, vol. 105, no. 19, p. 191104, 2014. C. Lee et al., "Dose assessment in dental cone-beam computed tomography: Comparison of optically stimulated luminescence dosimetry with Monte Carlo method," PloS one, vol. 15, no. 3, p. e0219103, 2020. P. Wang, X. Yan, D. Lui, W. Zhang, Y. Zhang, and X. Ma, "Detection of dental root fractures by using cone-beam computed tomography," Dentomaxillofacial Radiology, vol. 40, no. 5, pp. 290-298, 2011. N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, "Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography," Scientific reports, vol. 6, no. 1, p. 22079, 2016. J. George, "A feasibility evaluation of x-ray fluorescence emission tomography and x-ray luminescence tomography for real-time assessment of photodynamic therapy," 2016. H. Zhang et al., "Performance evaluation of the simplified spherical harmonics approximation for cone-beam x-ray luminescence computed tomography imaging," Journal of Innovative Optical Health Sciences, vol. 10, no. 03, p. 1750005, 2017. P. Feng, W. Cong, B. Wei, and G. Wang, "Analytic comparison between X-ray fluorescence CT and K-edge CT," IEEE Transactions on Biomedical Engineering, vol. 61, no. 3, pp. 975-985, 2013. J. Radon, "On the determination of functions from their integral values along certain manifolds," IEEE transactions on medical imaging, vol. 5, no. 4, pp. 170-176, 1986. R. Ng, "Fourier slice photography," in ACM Siggraph 2005 Papers, 2005, pp. 735-744. J. Beatty, "The radon transform and the mathematics of medical imaging," 2012. R. N. Bracewell and A. Riddle, "Inversion of fan-beam scans in radio astronomy," Astrophysical Journal, vol. 150, p. 427, vol. 150, p. 427, 1967. A. C. Kak and M. Slaney, Principles of computerized tomographic imaging. SIAM, 2001. J. W. Cooley and J. W. Tukey, "An algorithm for the machine calculation of complex Fourier series," Mathematics of computation, vol. 19, no. 90, pp. 297-301, 1965. L. Greengard and J.-Y. Lee, "Accelerating the nonuniform fast Fourier transform," SIAM review, vol. 46, no. 3, pp. 443-454, 2004. I. A. Elbakri and J. A. Fessler, "Efficient and accurate likelihood for iterative image reconstruction in X-ray computed tomography," in Medical Imaging 2003: Image Processing, 2003, vol. 5032: SPIE, pp. 1839-1850. A. H. Andersen and A. C. Kak, "Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm," Ultrasonic imaging, vol. 6, no. 1, pp. 81-94, 1984. L. A. Shepp and Y. Vardi, "Maximum likelihood reconstruction for emission tomography," IEEE transactions on medical imaging, vol. 1, no. 2, pp. 113-122, 1982. P. Christillin, "Nuclear Compton scattering," Journal of Physics G: Nuclear Physics, vol. 12, no. 9, p. 837, 1986. C.-K. Qiao, J.-W. Wei, and L. Chen, "An overview of the Compton scattering calculation," Crystals, vol. 11, no. 5, p. 525, 2021. J. B. Johnson, "Thermal agitation of electricity in conductors," Physical review, vol. 32, no. 1, p. 97, 1928. J. G. Proakis, Digital communications. McGraw-Hill, Higher Education, 2008. W. Schottky, "Über spontane Stromschwankungen in verschiedenen Elektrizitätsleitern," Annalen der physik, vol. 362, no. 23, pp. 541-567, 1918. R. Hui, Introduction to fiber-optic communications. Academic Press, 2019. Y. Liu, D. Tu, H. Zhu, and X. Chen, "Lanthanide-doped luminescent nanoprobes: controlled synthesis, optical spectroscopy, and bioapplications," Chemical Society Reviews, vol. 42, no. 16, pp. 6924-6958, 2013. D. Y. Kirsanova, Z. M. Gadzhimagomedova, A. Y. Maksimov, and A. V. Soldatov, "Nanomaterials for deep tumor treatment," Mini Reviews in Medicinal Chemistry, vol. 21, no. 6, pp. 677-688, 2021. C.-C. Hsu, S.-L. Lin, and C. A. Chang, "Lanthanide-doped core–shell–shell nanocomposite for dual photodynamic therapy and luminescence imaging by a single X-ray excitation source," ACS applied materials & interfaces, vol. 10, no. 9, pp. 7859-7870, 2018. H. Arkaban et al., "Polyacrylic acid nanoplatforms: Antimicrobial, tissue engineering, and cancer theranostic applications," Polymers, vol. 14, no. 6, p. 1259, 2022. V. Vitola, D. Millers, I. Bite, K. Smits, and A. Spustaka, "Recent progress in understanding the persistent luminescence in SrAl2O4: Eu, Dy," Materials Science and Technology, vol. 35, no. 14, pp. 1661-1677, 2019. T. Peng, H. Yang, X. Pu, B. Hu, Z. Jiang, and C. Yan, "Combustion synthesis and photoluminescence of SrAl2O4: Eu, Dy phosphor nanoparticles," Materials letters, vol. 58, no. 3-4, pp. 352-356, 2004. Z. Tang, F. Zhang, Z. Zhang, C. Huang, and Y. Lin, "Luminescent properties of SrAl2O4: Eu, Dy material prepared by the gel method," Journal of the European Ceramic Society, vol. 20, no. 12, pp. 2129-2132, 2000. A. Georgobiani, V. Gutan, V. Demin, and S. Semendyaev, "Luminescence and Optical-Memory model of SrAl 2 O 4: Eu 2+, Dy 3+ and Sr 4 Al 14 O 25: Eu 2+, Dy 3+," Inorganic Materials, vol. 45, pp. 1289-1294, 2009. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004. G. Bjontegaard, "Calculation of average PSNR differences between RD-curves," ITU SG16 Doc. VCEG-M33, 2001. L. R. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, no. 3, pp. 297-302, 1945. M. T. Terrovitis and R. G. Meyer, "Noise in current-commutating CMOS mixers," IEEE Journal of solid-state circuits, vol. 34, no. 6, pp. 772-783, 1999. L. Han and J. Xu, "Long exposure time noise in pinned photodiode CMOS image sensors," IEEE Electron Device Letters, vol. 39, no. 7, pp. 979-982, 2018. C. d. Santos, M. Tan, B. Xiang, and B. Zhou, "Attentive pooling networks," arXiv preprint arXiv:1602.03609, 2016. Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 111-118. V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, "Efficient processing of deep neural networks: A tutorial and survey," Proceedings of the IEEE, vol. 105, no. 12, pp. 2295-2329, 2017. G. Barnea, C. Dick, A. Ginzburg, E. Navon, and S. M. Seltzer, "A study of multiple scattering background in Compton scatter imaging," NDT & E International, vol. 28, no. 3, pp. 155-162, 1995. C. Bula et al., "Observation of nonlinear effects in Compton scattering," Physical Review Letters, vol. 76, no. 17, p. 3116, 1996. D. S.-C. Jin, L.-S. Chang, Y.-H. Wang, J.-C. Chen, S. H. Tseng, and T.-Y. Liu, "Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data," Biomedical Physics & Engineering Express, vol. 8, no. 6, p. 065021, 2022. H. Chen et al., "Low-dose CT with a residual encoder-decoder convolutional neural network," IEEE transactions on medical imaging, vol. 36, no. 12, pp. 2524-2535, 2017. O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical Image Computing and Computer Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 2015: Springer, pp. 234-241. D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014. L. Y. Pratt, "Discriminability-based transfer between neural networks," Advances in neural information processing systems, vol. 5, 1992. H. Shan et al., "3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network," IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1522-1534, 2018. K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," Journal of Big data, vol. 3, no. 1, pp. 1-40, 2016. H. Zhao, O. Gallo, I. Frosio, and J. Kautz, "Loss functions for image restoration with neural networks," IEEE Transactions on computational imaging, vol. 3, no. 1, pp. 47-57, 2016. C. Charrier, K. Knoblauch, L. T. Maloney, A. C. Bovik, and A. K. Moorthy, "Optimizing multiscale SSIM for compression via MLDS," IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4682-4694, 2012. S. Agostinelli et al., "GEANT4—a simulation toolkit," Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250-303, 2003. S. Jan et al., "GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy," Physics in Medicine & Biology, vol. 56, no. 4, p. 881, 2011. S. Vallières, Dose enhancement with nanoparticles in radiotherapy using gold doxorubicin conjugates. McGill University (Canada), 2017 G. Poludniowski, G. Landry, F. Deblois, P. M. Evans, and F. Verhaegen, "SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes," Physics in Medicine & Biology, vol. 54, no. 19, p. N433, 2009. J. H. Hubbell and S. M. Seltzer, "Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z= 1 to 92 and 48 additional substances of dosimetric interest," National Inst. of Standards and Technology-PL, Gaithersburg, MD (United States). Ionizing Radiation Div.,1995 H. Zhang, L. Waldmann, R. Manuel, H. Boije, T. Haitina, and A. Allalou, "zOPT: an open source optical projection tomography system and methods for rapid 3D zebrafish imaging," Biomedical optics express, vol. 11, no. 8, pp. 4290-4305, 2020. E. O. Brigham, The fast Fourier transform and its applications. Prentice-Hall, Inc., 1988. J.-C. Yoo and T. H. Han, "Fast normalized cross-correlation," Circuits, systems and signal processing, vol. 28, pp. 819-843, 2009. R. Fabbri, L. D. F. Costa, J. C. Torelli, and O. M. Bruno, "2D Euclidean distance transform algorithms: A comparative survey," ACM Computing Surveys (CSUR), vol. 40, no. 1, pp. 1-44, 2008. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88000 | - |
dc.description.abstract | X光激發光學與電腦斷層影像系統結合了X光激發光學和電腦斷層成像技術,為醫學和工業領域提供了一種非常有用的影像診斷工具,提供重要的影像診斷和分析資訊。其利用 X 光對物體進行成像,並同時檢測物體中的激發光信號,具有非侵入性的優點。然而受限於康普敦散射背景雜訊及感測器之熱雜訊,影像重建之分辨率受到限制。隨著深度學習演算法的成熟及電腦硬體的快速發展,近年來圖像降噪技術的性能得到了顯著進展。本研究建立一套 X 光激發光學與電腦斷層影像系統,撰寫反濾波投影及迭代重建演算法來對樣品進行影像三維重建,引入二值池化降噪減少感測器之熱雜訊,並透過深度學習 CCE-3D 模型來改善感測器因熱能、光量子的漲落、暗電流而產生的散粒雜訊,以及因康普敦散射而產生的背景雜訊,最後透過設計樣品及實驗來量化分析影像重建的品質,以Dice 相似性系数(DSC)、結構相似性係數(SSIM)、峰值信噪比(PSNR)等影像分析指標來探討影像三維重建的空間解析度,結果顯示我們成功提升影像重建品質,並將空間解析度提升至0.5 mm。 | zh_TW |
dc.description.abstract | The X-ray luminescence computed tomography (XLCT) system combines X-ray excitation optics with computerized tomography imaging technology, presenting a highly valuable image diagnostic tool for the medical and industrial domains. The integration facilitates the acquisition of vital image diagnosis and analysis information. By utilizing X-rays for object imaging while concurrently detecting excitation luminescence signals within the object, the XLCT system offers the advantage of non-invasiveness. However, limitations in image reconstruction resolution arise due to background noise from Compton scattering and thermal noise from the sensor. With the advancement of deep learning algorithms and the rapid progress of computer hardware, image-denoising technology has achieved significant development in recent years. This study establishes an X-ray excitation optics and CT imaging system, implementing a filter back projection(FBP) algorithm and simultaneous algebraic reconstruction technique (SART) for three -dimensional prosthesis image reconstruction. To mitigate sensor thermal noise, binary pooling noise reduction techniques are employed. Additionally, the deep learning CCE 3D model is utilized to suppress shot noise, dark current noise, and background noise resulting from Compton scattering. Furthermore, the design of prostheses and corresponding experiments enables quantitative analysis of image reconstruction quality, employing evaluation indexes such as the Dice similarity coefficient, structural similarity coefficient, and peak signal-to-noise ratio to assess the spatial resolution of three- dimensional image reconstruction. The results demonstrate the successful improvement of image reconstruction quality, with the achieved spatial resolution reaching 0.5 mm. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-01T16:19:38Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-01T16:19:38Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書...........................................................................................................#
誌謝................................................................................................................................... i 中文摘要.......................................................................................................................... ii ABSTRACT .................................................................................................................... iii CONTENTS .................................................................................................................... iv LIST OF FIGURES........................................................................................................ vii LIST OF TABLES......................................................................................................... xiii Chapter 1 Introduction..............................................................................................1 1.1 Background and motivation............................................................................1 1.2 Research aims and objectives.........................................................................2 1.3 Structure of the dissertation............................................................................3 Chapter 2 Background Knowledge ..........................................................................5 2.1 Historical background.....................................................................................5 2.2 Theorem of image reconstruction technology ................................................6 2.2.1 Image reconstruction algorithm ............................................................7 2.2.2 Fluorescence object function.................................................................7 2.2.3 Radon transform....................................................................................8 2.2.4 Fourier slice theorem...........................................................................10 2.3 Filter Back Projection (FBP) ........................................................................13 2.4 Simultaneous Algebraic Reconstruction Technique (SART)........................15 2.5 Compton scattering.......................................................................................19 2.6 Source of photodetection noise.....................................................................22 2.6.1 Thermal noise......................................................................................22 2.6.2 Shot noise ............................................................................................23 2.6.3 Dark current noise ...............................................................................24 Chapter 3 Research Design and Methods..............................................................25 3.1 X-ray luminescence computed tomography system.....................................25 3.1.1 XLCT prototype setup.........................................................................25 3.1.2 Computer Environment for Image Reconstruction .............................28 3.1.3 Fluorescent material ............................................................................29 3.2 Image quality analysis index ........................................................................31 3.2.1 Structural Similarity Index (SSIM).....................................................31 3.2.2 Peak Signal-to-Noise Ratio (PSNR) ...................................................33 3.2.3 Dice Similarity Coefficient (DSC)......................................................34 3.3 Experiment methodology and process..........................................................35 3.4 Noise reduction method................................................................................38 3.4.1 Binary pooling noise reduction method ..............................................38 3.4.2 Deep learning noise reduction method................................................44 3.5 Global center-of-rotation correction method (gCOR) ..................................57 Chapter 4 Results and Discussion...........................................................................60 4.1 Comparison of FBP and SART algorithm ....................................................60 4.1.1 Comparison of reconstruction efficiency ...........................................60 4.1.2 Comparison of noise resistance in reconstruction..............................63 4.2 Image reconstruction quality analysis...........................................................68 4.2.1 Analysis of image pre-processing improvement ................................68 4.2.2 Analysis of prosthesis placement correction ......................................78 4.3 Spatial resolution analysis.............................................................................87 4.3.1 Optical tomography experiment – millimeter scale ...........................87 4.3.2 Optical tomography experiment – sub-millimeter scale ....................94 Chapter 5 Conclusions and Future Works.............................................................100 5.1 Conclusions.................................................................................................100 5.2 Future works ...............................................................................................102 REFERENCE ................................................................................................................103 | - |
dc.language.iso | en | - |
dc.title | 應用深度學習降噪暨影像三維重建分析 | zh_TW |
dc.title | Application of Deep Learning for Denoising and Three-Dimensional Image Reconstruction Analysis in X-ray Luminescence Computed Tomography System | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 蕭惠心;黃定洧 | zh_TW |
dc.contributor.oralexamcommittee | Hui-Hsin HSIAO;Ding-Wei Huang | en |
dc.subject.keyword | 深度學習,影像處理,三維重建,降噪, | zh_TW |
dc.subject.keyword | deep learning,image processing,3D reconstruction,noise reduction, | en |
dc.relation.page | 110 | - |
dc.identifier.doi | 10.6342/NTU202301184 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-07-05 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 光電工程學研究所 | - |
顯示於系所單位: | 光電工程學研究所 |
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