(3.238.174.50) 您好!臺灣時間:2021/04/20 22:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳亮瑜
研究生(外文):Liang-Yu Chen
論文名稱(外文):Reconstruction and Evaluation of Diffuse Optical Imaging
指導教授:潘敏俊
學位類別:博士
校院名稱:國立中央大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:99
中文關鍵詞:擴散光學斷層影像邊界保持正規化Tikhonov 正規化contrast-and-size detail 分析contrast detial 分析
外文關鍵詞:diffuse optical tomographyedge-preserving regularizationTikhonov regularizationcontrast-and-size detail analysiscontrast detail analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:165
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:20
  • 收藏至我的研究室書目清單書目收藏:0
在本研究中,邊界保持正規化 (edge-preserving regularization) 首次被提議用來克服 擴散光學成像影像重建中的病態問題,避免利用 Tikhonov 正規化時,重建影像中不易 區分腫瘤與背景組織的邊界模糊化現像。在所提出的邊界保持正規化方法中,有邊界 保持特性的潛能函式被視為一調整項並引入至目標函式中;為了最小化所提出的目 標函式,採用半二次正規化 (half-quadratic regularization) 來簡化最佳化工作,並使用 疊代方式求解此最佳化問題。本研究中亦呈現其邊界保持正規化重建演算法具有相 當的彈性,該演算法中可採用不同之權重函式 (weighting function),包括:泛羅倫茲 函式 (generalized Lorentzian function)、指數函式 (exponential function) 和泛總變差函式 (generalized total variation function)。在結果中,利用數值模擬數據與實驗數據來驗證所 提議之方法;從重建影像得知,使用邊界保持正規化,比 Tikhonov 正規化方法的使用, 有較佳的影像品質;除此之外,基於吸收係數影像可進一步擴展成功能性影像之故, 在擴散光學成像影像重建中,建議使用泛羅倫茲權重函式之邊界保持正規化影像重建 演算法。
為評估近紅外光擴散光學斷層影像之成像品質,本研究中提出客觀的 contrast-and- size detail (CSD) 分析方法,其概念源自於主觀的 contrast detial (CD) 分析方法。在 CSD 分析方法中,定義並利用了數值量化的光學對比解析度與尺寸解析度,來評估不同光 學對比度與尺寸內置物下之成像品質,且藉由影像灰階值顯示之方法,呈現影像評估 結果;除此之外,亦可計算固定光學對比、尺寸內置物下之平均尺寸解析度與光學對 比解析度,並繪製 CSD 解析度曲線,來評估不同成像方式之影像結果。在結果中,採 用 Tikhonov 正規化,與不同權重函式下之邊界保持正規化影像重建演算法,呈現此 CSD 分析方法之應用方式;評估結果顯示,使用泛羅倫茲權重函式之邊界保持正規化影像重建演算法,吸收係數影像有較佳之重建結果。
In this study, we first propose the use of edge-preserving regularization in optimizing an ill-conditioned problem in the reconstruction procedure for diffuse optical tomography to prevent unwanted edge smoothing, which usually degrades the attributes of images for distinguishing tumors from background tissues when using Tikhonov regularization. In the edge-preserving regularization method presented here, a potential function with edge-preserving properties is introduced as a regularized term in an objective function. In order to minimize this proposed objective function, an iterative method solving this optimization problem is presented in which half-quadratic regularization is introduced to simplify the minimization task. Both numerical and experimental data are employed to justify the proposed technique. The reconstruction results indicate that the edge-preserving regularization performs superior to Tikhonov regularization.
A flexible edge-preserving regularization algorithm based on the finite element method is proposed to reconstruct the optical-property images of near infrared diffuse optical tomography. This regularization algorithm can easily incorporate with varied weighting functions, such as a generalized Lorentzian function, an exponential function, or a generalized total variation function. To evaluate the performance, results obtained from Tikhonov or edge-preserving regularization are compared with each other. As found, the edge-preserving regularization with the generalized Lorentzian function is more attractive than that with other functions for the estimation of absorption-coefficient images concerning functional tomographic images to discover functional information of tested phantoms/tissues.
Based on the concept derived from the subjective contrast detail (CD) analysis, an objective contrast-and-size detail (CSD) analysis for evaluating the image quality of near infrared diffuse optical tomography (NIR DOT) is proposed. We define a measure for numerical CSD analysis based on the resolution estimation of contrast and size. Following that, the contrast-and-size map of resolution can be calculated and displayed for each corresponding image in the map; furthermore, a CSD resolution curve can be characterized by calculating the average value of the projection along the physical quantity/axis (size or contrast). To provide some worked examples about the proposed CSD analysis evaluating the imaging performance of different reconstruction methods, Tikhonov regularization and edge-preserving regularization with different weighting functions were employed. Results suggested that using edge-preserving regularization with the generalized Lorentzian weighting function is the most attractive for the estimation of absorption-coefficient images.
摘要 i
Abstract iii
誌謝 v
1 Introduction 1
1.1 Background.................................... 2
1.2 Literature review ................................. 4
1.2.1 Diffuse optical tomography ....................... 4
1.2.2 Edge-preserving regularization...................... 6
1.2.3 Contrast-detail analysis.......................... 8
1.3 The purpose of this study............................. 10
2 Theoretical model and algorithm in diffuse optical imaging 12
2.1 PN approximation to the radiation transport equation . . . . . . . . . . . . . . 12
2.2 P1 approximation — the diffusion equation ................... 15
2.3 Forward solution to the diffusion equation — the finite element method . . . . 17
2.3.1 Generation of the simulation data for image reconstruction . . . . . . . 21
3 Inverse solution for image reconstruction 24
3.1 Formulation of inverse problem in DOI ..................... 24
3.2 Construction of the Jacobian matrix ....................... 25
3.2.1 The direct method ............................ 26
3.2.2 The adjoint method............................ 26
3.3 Normalization of the Jacobian matrix....................... 27
4 Regularization in inverse solution 29
4.1 Tikhonov regularization.............................. 29
4.2 Edge-preserving regularization.......................... 30
4.2.1 Implementation in image reconstruction for DOT . . . . . . . . . . . . 32
4.3 Results and discussion .............................. 35
4.3.1 Reconstructions from simulated data................... 37
4.3.2 Reconstructions from experimental data . . . . . . . . . . . . . . . . . 42
5 Edge-preserving regularization with different weighting functions 49
5.1 Varied weighting functions in edge-preserving regularization . . . . . . . . . . 49
5.1.1 Evaluation method ............................ 50
5.1.2 Numerical simulation........................... 51
5.1.3 Experimental trials ............................ 53
5.2 Visualized numerical assessment with contrast-and-size detail analysis . . . . . 60
5.2.1 Contrast-detailanalysis.......................... 62
5.2.2 Contrast-and-size detail analysis based on the numerical assessment . . 65
6 Conclusion and future work 70
[1] World Health Statistics 2012. Geneva, World Health Organization, 2012.
[2] L.Tabar,M.F.Yen,B.Vitak,H.T.Chen,R.A.Smith,andS.W.Duffy,“Mammography service screening and mortality in breast cancer patients: 20-year followup before and after introduction of screening,” Lancet 361, 1405–1410 (2003).
[3] J. G. Elmore, M. B. Barton, V. M. Moceri, S. Polk, P. J. Arena, and S. W. Fletcher, “Ten- year risk of false positive screening mammograms and clinical breast examinations,” The New England Journal of Medicine 338, 1089–1096 (1998).
[4] P. T. Huynh, A. M. Jarolimek, and S. Day, “The false-negative mammogram,” Radio- graphics 18, 1137–1154 (1998).
[5] A. Gibson, and H. Dehghani, “Diffuse optical imaging,” Phil. Trans. R. Soc. A 367, 3055–3072 (2009).
[6] D.R.Leff,O.J.Warren,L.C.Enfield,A.Gibson,T.Athanasiou,D.K.Patten,J.Hbden, G. Z. Yang, and A. Darzi, “Diffuse optical imaging of the healthy and diseased breast: A systematic review,” Breast Cancer Res. Treat. 108, 9–22 (2008).
[7] J. C. Hebden, S. R. Arridge, and D. T. Delpy, “Optical imaging in medicine: I. Experi- mental techniques,” Phys. Med. Biol. 42, 825-840 (1997).
[8] H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Phil. Trans. R. Soc. A 367, 3073- 3093 (2009).
[9] S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Problems 25, 123010 (2009).
[10] T.Durduran,R.Choe,W.B.Baker,andA.G.Yodh,“Diffuseopticsfortissuemonitoring and tomography,” Rep. Prog. Phys. 73, 076701 (2010).
[11] B.W.Pogue,M.S.Patterson,H.Jiang,andK.D.Paulsen,“Initialassessmentofasimple system for frequency-domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709– 1729 (1995).
[12] S. R. Arridge, M. Schweiger, M. Hiraoka, and D. T. Delpy, “A finite element approach for modeling photon transport in tissue,” Med. Phys. 20, 299–309 (1993).
[13] K. D. Paulsen, and H. Jiang, “Spatially varying optical property reconstruction using a finite element diffusion equation approximation,” Med. Phys. 22, 691–701 (1995).
[14] M. A. O’Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of het- erogeneous turbid media by frequency-domain diffusing-photon tomography,” Optics Letters 20, 426–428 (1995).
[15] M. A. O’Leary, “Imaging with diffuse photon density waves,” Dissertation in Physics, University of Pennsylvania (1996).
[16] A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image recon- struction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18, 262–271 (1999).
[17] S.R.Arridge,“Opticaltomographyinmedicalimaging,”InverseProblems15,R41–R93 (1999).
[18] H.Dehghani,M.E.Eames,P.K.Yalavarthy,S.C.Davis,S.Srinivasan,C.M.Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25, 711–732 (2008).
[19] D.A.Boas,A.M.Dale,andM.A.Franceschini,“Diffuseopticalimagingofbrainactiva- tion: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23, S275–S288 (2004).
[20] A. H. Hielscher, A. Y. Bluestone, G. S. Abdoulaev, A. D. Klose, J. Lasker, M. Stewart, U. Netz, and J. Beuthan, “Near-infrared diffuse optical tomography,” Dis. Markers 18, 313–337 (2002).
[21] A. Neumaier, “Solving ill-conditioned and singular linear systems: a tutorial on regular- ization,” SIAM Rev. 40, 636–666 (1998).
[22] Q.Zhao,L.Ji,andT.Jiang,“Improvingperformanceofreflectancediffuseopticalimag- ing using a multicentered mode,” J. Biomed. Opt. 11, 064019 (2006).
[23] K. Uludag, J. Steinbrink, A. Villringer, and H. Obrig, “Separability and cross talk: opti- mizing dual wavelength combinations for near-infrared spectroscopy of the adult head,” NeuroImage 22, 583–589 (2004).
[24] J. Wang, S. C. Davis, S. Srinivasan, S. Jiang, B. W. Pogue, and K. D. Paulsen, “Spectral tomography with diffuse near-infrared light: inclusion of broadband frequency domain spectral data,” J. Biomed. Opt. 13, 041305 (2008).
[25] A. Pifferi, P. Taroni, A. Torricelli, F. Messina, R. Cubeddu, and G. Danesini, “Four- wavelength time-resolved optical mammography in the 680-980-nm range,” Optics Let- ters 28, 1138–1140 (2003).
[26] A. Corlu, T. Durduran, R. Choe, M. Schweiger, E. Hillman, S. Arridge, and A. Yodh, “Uniqueness and wavelength optimization in continuous-wave multispectral diffuse op- tical tomography,” Optics Letters 28, 2339–2341 (2003).
[27] M. E. Eames, J. Wang, B. W. Pogue, and H. Dehghani, “Wavelength band optimiza- tion in spectral near-infrared optical tomography improves accuracy while reducing data acquisition and computational burden,” J. Biomed. Opt. 13, 054037 (2008).
[28] B.BrendelandT.Nielsen,“Selectionofoptimalwavelengthsforspectralreconstruction in diffuse optical tomography,” J. Biomed. Opt. 14, 034041 (2009).
[29] Q. Zhang, T. Brukilacchio, A. Li, J. Stott, T. Chaves, E. Hillman, T. Wu, M. Chorlton, E. Rafferty, R. Moore, D. Kopans, and D. Boas, “Coregistered tomographic x-ray and optical breast imaging: initial results,” J. Biomed. Opt. 10, 024033 (2005).
[30] Z. Yuan, Q. Zhang, E. S. Sobel, and H. Jiang, “Tomographic x-ray-guided three- dimensional diffuse optical tomography of osteoarthritis in the finger joints,” J. Biomed. Opt. 13, 044006 (2008).
[31] M.Holboke,B.Tromberg,X.Li,N.Shah,J.Fishkin,D.Kidney,J.Butler,B.Chance,and A. Yodh, “Three-dimensional diffuse optical mammography with ultrasound localization in a human subject,” J. Biomed. Opt. 5, 237–247 (2000).
[32] Q. Zhu, S. Tannenbaum, P. Hegde, M. Kane, C. Xu, and S. Kurtzman, “Noninvasive monitoring of breast cancer during neoadjuvant chemotherapy using optical tomography with ultrasound localization,” Neoplasia 10, 1028–1040 (2008).
[33] Z. Jiang, D. Piao, G. Xu, J. W. Ritchey, G. R. Holyoak, K. E. Bartels, C. F. Bunting, G. Slobodov, and J. S. Krasinki, “Trans-rectal ultrasound-coupled near-infrared opti- cal tomography of the prostate part ii: Experimental demonstration,” Opt. Express 16, 17505–17520 (2008).
[34] V.Ntziachristos,A.Yodh,M.Schnall,andB.Chance,“MRI-guideddiffuseopticalspec- troscopy of malignant and benign breast lesions,” Neoplasia 4, 347–354 (2002).
[35] H. Dehghani, B. Pogue, B. Brooksby, S. Srinivasan, and K. Paulsen, “Image reconstruc- tion strategies using dual modality MRI-NIR data,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE, 2006), pp. 682–685.
[36] P. Hiltunen, S. J. D. Prince, and S. Arridge, “A combined reconstruction-classification method for diffuse optical tomography,” Phys. Med. Biol. 54, 6457–6476 (2009).
[37] A. Li, G. Boverman, Y. Zhang, D. Brooks, E. Miller, M. Kilmer, Q. Zhang, E. Hillman, and D. Boas, “Optimal linear inverse solution with multiple priors in diffuse optical to- mography,” Appl. Opt. 44, 1948–1956 (2005).
[38] S. Srinivasan, B. Pogue, B. Brooksby, S. Jiang, H. Dehghani, C. Kogel, W. Wells, S. Poplack, and K. Paulsen, “Near-infrared characterization of breast tumors in vivo using spectrally-constrained reconstruction,” Technol. Cancer Res. Treat. 4, 513–526 (2005).
[39] J. Kaipio, V. Kolehmainen, M. Vauhkonen, and E. Somersalo, “Inverse problems with structural prior information,” Inverse Probl. 15, 713–729 (1999).
[40] A. Hielscher and S. Bartel, “Parallel programming of gradient-based iterative image re- construction schemes for optical tomography,” Comput. Methods Programs Biomed. 73, 101–113 (2004).
[41] A. Douiri, M. Schweiger, J. Riley, and S. R. Arridge, “Anisotropic diffusion regulariza- tion methods for diffuse optical tomography using edge prior information,” Meas. Sci. Technol. 18, 87–95 (2007).
[42] B. Pogue, T. McBride, J. Prewitt, U. Osterberg, and K. Paulsen, “Spatially variant regu- larization improves diffuse optical tomography,” Appl. Opt. 38, 2950–2961 (1999).
[43] H. Niu, P. Guo, L. Ji, Q. Zhao, and T. Jiang, “Improving image quality of diffuse optical tomography with a projection-error-based adaptive regularization method,” Opt. Express 16, 12423–12434 (2008).
[44] N.Cao,A.Nehorai,andM.Jacob,“Imagereconstructionfordiffuseopticaltomography using sparsity regularization and expectation-maximization algorithm,” Opt. Express 15, 13695–13708 (2007).
[45] Y. Pei, H. Graber, and R. Barbour, “Normalized-constraint algorithm for minimizing inter-parameter crosstalk in dc optical tomography,” Opt. Express 9, 97–109 (2001).
[46] Y.Xu,X.Gu,T.Khan,andH.Jiang,“Absorptionandscatteringimagesofheterogeneous scattering media can be simultaneously reconstructed by use of dc data,” Appl. Opt. 41, 5427–5437 (2002).
[47] M. E. Eames and H. Dehghani, “Wavelength dependence of sensitivity in spectral dif- fuse optical imaging: effect of normalization on image reconstruction,” Opt. Express 16, 17780–17791 (2008).
[48] M. C. Pan, C. H. Chen, L. Y. Chen, M. C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).
[49] P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133-144 (2005).
[50] V.B.S.PrasathandA.Singh,“Ahybridconvexvariationalmodelforimagerestoration,” Applied Mathematics and Computation 215, 3655-3664 (2010).
[51] M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Computer Vision and Image Understanding 88, 76-93 (2002).
[52] D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image de- noising,” Journal of Computational and Applied Mathematics 210, 222-231 (2007).
[53] A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized recon- struction: an application to limited-angle tomography,” IEEE Transactions on Image Processing 7, 204-221 (1998).
[54] R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Transactions on Image Processing 15, 3728-3735 (2006).
[55] H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance in- version,” Geophysical Prospecting 55, 819-833 (2007).
[56] H.Zhang,Z.Shang,andC.Yang,“Adaptivereconstructionmethodofimpedancemodel with absolute and relative constraints,” Journal of Applied Geophysics 67, 114-124 (2009).
[57] G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split- gradient method to 3D image deconvolution in fluorescence microscopy,” Journal of Microscopy 234, 47-61 (2009).
[58] X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regular- ization in image restoration,” Journal of Computational and Applied Mathematics 225, 478-486 (2009).
[59] R.Zanella,P.Boccacci,L.Zanni,andM.Bertero,“Efficientgradientprojectionmethods for edge-preserving removal of Poisson noise,” Inverse Problems 25, 045010 (2009).
[60] A.Jalobeanu,L.Blance-Feraud,andJ.Zerubia,“Hyperparameterestimationforsatellite image restoration using a MCMC maximum-likelihood method,” Pattern Recognition 35, 341-352 (2002).
[61] P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Problems 13, 403-410 (1997).
[62] J.M.Bardsley,andJ.Goldes,“Aniterativemethodforedge-preservingMAPestimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171-185 (2010).
[63] B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Transactions on Antennas and Propagation 59, 3710-3718 (2011).
[64] N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Transactions on Medical Imaging 22, 1275-1287 (2003).
[65] D. F. Yu and J. A. Fessler, “Three-dimensional non-local edge-preserving regulariza- tion for PET transmission reconstruction,” Proc. IEEE Nuclear Science Symp. Medical Imaging Conf. 2, 1566-1570 (2000).
[66] C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for im- age classification and restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 460-472 (2000).
[67] R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge- preserving regularization,” Physiol. Meas. 25, 195-207 (2004).
[68] L.Blanc-FeraudandM.Barlaud,“Edgepreservingrestorationofastrophysicalimages,” Vistas in Astronomy 40, 531-538 (1996).
[69] S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlau, “Variational approach for edge- preserving regularization using coupled PDE’s,’ IEEE Transactions on Image Processing 7, 387-397 (1998).
[70] S. Rivetti, N. Lanconelli, R. Campanini, M. Bertolini, G. Borasi, A. Nitrosi, C. Danielli, L. Angelini, S. Maggi, “Comparison of different commercial FFDM units by means of physical characterization and contrast-detail analysis,” Med. Phys. 33, 4198-4209 (2006).
[71] B. Cederstrom, U. Streubuhr, “Comparison of photo-counting to storage phosphor plate mammography using contrast-detail phantom analysis,” Nucl. Instrum. Methods Phys. Res. Sect. A 580, 1101-1104 (2007).
[72] S. Rivetti, N. Lanconelli, M. Bertolini, A. Nitrosi, A. Burani, D. Acchiappati, “Compar- ison of different computed radiography systems: physical characterization and contrast detail analysis,” Med. Phys. 37, 440-448 (2010).
[73] G. Borasi, E. Samei, M. Bertolini, A. Nitrosi, D. Tassoni, “Contrast-detail analysis of three flat panel detectors for digital radiography,” Med. Phys. 33, 1707-1719 (2006).
[74] W. J. H. Veldkamp, L. J. M. Kroft, M. V. Boot, B. J. A. Mertens, J. Geleijns, “Contrast- detail evaluation and dose assessment of eight digital chest radiography systems in clin- ical practice,” Eur. Radiol. 16, 333-341 (2006).
[75] P. F. Judy, R. G. Swensson, R. D. Nawfel, K. H. Chan, S. E. Seltzer, “Contrast-detail curves for liver CT,” Med. Phys. 19, 1167-1174 (1992).
[76] E. Samei, N. T. Ranger, D. M. Delong, “A comparative contrast-detail study of five medical displays,” Med. Phys. 35, 1358-1364 (2008).
[77] M. Yamaguchi, H. Fujita, Y. Bessho, T. Inoue, Y. Asai, K. Murase, “Investigation of op- timal display size for detecting ground-glass opacity on high resolution computed tomog- raphy using a new digital contrast-detail phantom,” Eur. J. Radiol. 80, 845-850 (2011).
[78] T.J.Hall,M.F.Insana,N.M.Soller,L.A.Harrison,“Ultrasoundcontrast-detailanalysis: a preliminary study in human observer performance,” Med. Phys. 20, 117-127 (1993).
[79] A. Pascoal, C. P. Lawinski, I. Honey, P. Blake, “Evaluation of a software package for automated quality assessment of contrast detail images — comparison with subjective visual assessment,” Phys. Med. Biol. 50, 5743-5757 (2005).
[80] B. W. Pogue, S. C. Davis, X. Song, B. A. Brooksby, H. Dehghani, K. D. Paulsen, “Im- age analysis methods for diffuse optical tomography,” J. Biomed. Opt. 11, 033001-1-16 (2006).
[81] B. W. Pogue, C. Willscher, T. O. McBride, U. L. Osterberg, K. D. Paulsen, “Contrast- detail analysis for detection and characterization with near-infrared diffuse tomography,” Med. Phys. 27, 2693-2700 (2000).
[82] S. C. Davis, B. W. Pogue, H. Dehghani, K. D. Paulsen, “Contrast-detail analysis charac- terizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10, 050501-1-3 (2005).
[83] H. R. Ghadyani, S. Srinivasan, B. W. Pogue, K. D. Paulsen, “Characterizing accuracy of total hemoglobin recovery using contrast-detail analysis in 3D image-guided near in- frared spectroscopy with the boundary element method,” Opt. Express 18, 15917-15935 (2010).
[84] M. Pan, L. Chen, M. Pan, and C. Chen, “Inverse solution regularized with the edge- preserving constraint for NIR DOT,” in Biomedical Optics, OSA Technical Digest (CD) (Optical Society of America, 2008), paper PDPBMD1.
[85] L. Chen, M. Pan, and M. Pan, “Frequency-domain diffuse optical tomography imple- mented with edge-preserving regularization,” in Biomedical Optics, OSA Technical Di- gest (CD) (Optical Society of America, 2010), paper BME7.
[86] P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge- preserving regularization in computed imaging,” IEEE Transactions on Image Processing 6, 298–311 (1997).
[87] L. Y. Chen, M.-Chun Pan, and M.-Cheng Pan, “Implementation of edge-preserving reg- ularization for frequency-domain diffuse optical tomography,” Applied Optics 51, 43-54 (2012).
[88] S.R.Arridge,andM.Schweiger,“Photon-measurementdensityfunctions.Part2:Finite- element-method calculations,” Applied Optics 34, 8026–8037 (1995).
[89] M. C. Pan, C. H. Chen, M. C. Pan, and Y. M. Shyr, “Near infrared tomographic sys- tem based on high angular resolution mechanism- design, calibration, and performance,” Measurement 42, 377–389 (2009).
[90] K. M. Case, and P. F. Zweifel, Linear Transport Theory, Addison-Wesley, Boston, 1967.
[91] D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid me- dia: theory and biomedical applications,” Dissertation in Physics, University of Penn- sylvania (1996).
[92] G. B. Arfken, and H. J. Weber, Mathematical Methods for Physicists, Academic Press, New York, 2005.
[93] K. Furutsu, and Y. Yamada, “Diffusion approximation for a dissipative random medium and the applications,” Phys. Rev. E 50, 3634–3640 (1994).
[94] H. Jiang, Diffuse Optical Tomography: Principles and Applications, CRC Press, Boca Raton, 2010.
[95] S. Srinivasan, B. W. Pogue, H. Dehghani, S. Jiang, X. Song, and K. D. Paulsen, “Im- proved quantification of small objects in near-infrared diffuse optical tomography,” J. Biomed. Opt. 9, 1161–1171 (2004).
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔