扩散张量数据处理及白质纤维追踪算法的研究
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摘要
扩散张量成像(DTI)作为磁共振成像(MRI)研究上的一项重大技术进展和突破,是目前唯一可以无创地、清晰地显示人脑内部组织的纤维结构的技术。它通过对大脑白质区域水分子各向异性扩散信息的表达来研究大脑白质纤维的走向和三维结构信息。
本课题基于人脑磁共振扩散张量图像数据集,对扩散张量数据集的处理、显示方法和白质纤维追踪算法进行了研究,主要研究内容、成果如下:
首先,在对扩散张量成像原理进行充分理解的基础上,对扩散张量的数据集的可视化方法进行了研究,包括颜色编码法、图元显示法、纤维追踪法和体显示法。其中重点对图元显示法中不同的图元对不同区域、不同大小的DTI数据集进行了显示,总结了不同图元各自的优缺点,并采用实际数据进行方法性验证。
其次,对白质束纤维追踪的算法进行了深入研究,在总结前人所提出的追踪算法的优缺点基础上,分析这些算法不能解决纤维的交叉分支问题的原因,而快速行进算法能够解决该问题,但伴随而来的是假性阳支的出现,所以本论文在前人的基础之上,提出了改进的快速行进算法,通过不同体素之间的演化,定义不同的速度函数,使得新算法具有可靠的理论基础。
最后,提出将三线性插值算法运用到白质束追踪当中。通过VTK(Visual Toolkit)软件包进行仿真,实现了对该算法在纤维追踪上的运用,并且得到了较好的追踪结果。为后续进行DTI数据集仿真奠定了一个良好的理论基础。
Diffusion Tensor Imaging (DTI) which as one of the major breakthrough of the research on Magnetic Resonance Imaging (MRI) is the unique noninvasive in vivo imaging technology to generate fiber-tract trajectories within the white matter of human brain. Through the showing of water molecules in brain white matter area, DTI is used to research the fiber direction of the brain nerve and its three dimensions configuration.
This thesis bases on the MRI data set has accomplish the research on the processing and rending method of DTI data set and white matter fiber tracking algorithm, the main work and achievement can be concluded as following:
Firstly, on the basis of fully understanding of the DTI principle, this thesis has studied the method of visualization of DTI data set, including Color coding, Glyphing, White matter tractography and Volume Rending. The key work of this part is the rending of DTI data set of different region and size with different glyphs, and then the advantages and disadvantages of these glyphs are concluded. Also, the conclusions have been confirmed by experimental results of the real DTI data set.
Secondly, this thesis has researched deeply the white matter fiber tracking algorithm. Based on reviewing the advantages of the algorithms introduced by predecessors, the issue that those algorithms can’t solve the problem of the crossing and branching of white matter fibers is analysed, yet Fast Marching Method (FMM) can solve the problem. But FMM gets a lot of false pathway, so this thesis has proposed an improved FMM for FT, which defines different speed function according to the different evolving between the voxels, so the improved FMM has the reliable theory foundation.
Finally, we proposed the tri-linear interpolation algorithm for fiber tracking (FT). And we have achieved the white matter fibers simulation with VTK (Visual Toolkit), the result is coincide with the real fibers by and large. The work can be the theoretical basis of future research.
本课题基于人脑磁共振扩散张量图像数据集,对扩散张量数据集的处理、显示方法和白质纤维追踪算法进行了研究,主要研究内容、成果如下:
首先,在对扩散张量成像原理进行充分理解的基础上,对扩散张量的数据集的可视化方法进行了研究,包括颜色编码法、图元显示法、纤维追踪法和体显示法。其中重点对图元显示法中不同的图元对不同区域、不同大小的DTI数据集进行了显示,总结了不同图元各自的优缺点,并采用实际数据进行方法性验证。
其次,对白质束纤维追踪的算法进行了深入研究,在总结前人所提出的追踪算法的优缺点基础上,分析这些算法不能解决纤维的交叉分支问题的原因,而快速行进算法能够解决该问题,但伴随而来的是假性阳支的出现,所以本论文在前人的基础之上,提出了改进的快速行进算法,通过不同体素之间的演化,定义不同的速度函数,使得新算法具有可靠的理论基础。
最后,提出将三线性插值算法运用到白质束追踪当中。通过VTK(Visual Toolkit)软件包进行仿真,实现了对该算法在纤维追踪上的运用,并且得到了较好的追踪结果。为后续进行DTI数据集仿真奠定了一个良好的理论基础。
Diffusion Tensor Imaging (DTI) which as one of the major breakthrough of the research on Magnetic Resonance Imaging (MRI) is the unique noninvasive in vivo imaging technology to generate fiber-tract trajectories within the white matter of human brain. Through the showing of water molecules in brain white matter area, DTI is used to research the fiber direction of the brain nerve and its three dimensions configuration.
This thesis bases on the MRI data set has accomplish the research on the processing and rending method of DTI data set and white matter fiber tracking algorithm, the main work and achievement can be concluded as following:
Firstly, on the basis of fully understanding of the DTI principle, this thesis has studied the method of visualization of DTI data set, including Color coding, Glyphing, White matter tractography and Volume Rending. The key work of this part is the rending of DTI data set of different region and size with different glyphs, and then the advantages and disadvantages of these glyphs are concluded. Also, the conclusions have been confirmed by experimental results of the real DTI data set.
Secondly, this thesis has researched deeply the white matter fiber tracking algorithm. Based on reviewing the advantages of the algorithms introduced by predecessors, the issue that those algorithms can’t solve the problem of the crossing and branching of white matter fibers is analysed, yet Fast Marching Method (FMM) can solve the problem. But FMM gets a lot of false pathway, so this thesis has proposed an improved FMM for FT, which defines different speed function according to the different evolving between the voxels, so the improved FMM has the reliable theory foundation.
Finally, we proposed the tri-linear interpolation algorithm for fiber tracking (FT). And we have achieved the white matter fibers simulation with VTK (Visual Toolkit), the result is coincide with the real fibers by and large. The work can be the theoretical basis of future research.
引文
[1] Basser PJ, Mattiello J, Turner R, et al. Diffusion tensor echo-planar imaging of human brain. Proceedings of the SMRM, 1993, 56(5): 584~562
[2] Mukherjee P, Miller JH, Shimony JS, et al. Difusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol, 2002, 23(9): 1445~1456
[3] Wang S, Poptani H, Bilello M, et al. Diffusion tensor imaging in amyotrophic lateral scleorsis: volumetric analysis of the corticospinal tract. AJNR Am J Neuroradiol, 2006, 27(6): 1234~1238
[4] Sinha S, Sinha U. In vivo diffusion tensor imaging of the human prostate. Magnetic resonance in medicine, 2004, 52(3): 530~537
[5] Casanova MF. Neuorpathological and genetic findings in autism: the significance of a putative minicolumnopathy. The Neuroscientist, 2006, 12(5): 435~441
[6] Ashtari M, Kumra S, Bhaskar SL. Attention-deficit hyperactivity disorder: a preliminary diffusion tensor imaging study. Biological psychiatry, 2005, 57(5): 448~455
[7] Kubicki M, Park H, Westin CF, et al. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. Neurolmage, 2005, 26(4): 1109~1118
[8] Basser PJ, Pajevic S, Pierpaolic, et al .In vivo fiber tractography using DT-MRI data. Magnetic resonance in medicine, 2000, 44(4): 625~632
[9] Masutani Y, Aoki S, Abe O, et al. MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. European journal of radiology, 2003, 46(1): 53~66
[10] Sethian, JA. A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci, 1996, 93: 1591~1595
[11] Parker GJ, Wheeler-Kingshott CA, Barker GJ. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE transsctions on medical imaging, 2002, 21(5): 505~512
[12] Kumazawa S, Yoshiura T, Arimura H, et al. White matter fiber tractography based on a directional diffusion field in diffusion tensor MRI. Proceeding of SPIE, 2006, 61(44): 1260~1268
[13] Watts R, Liston C , Niogi S, et al. Fiber tracking using magnetic resonance diffusion tensor imaging and its applications to human brain development. Mental Retardation and Developmental Disabilities Research Reviews, 2003,9(3): 168~ 191
[14] Le BD, Breton E. In vivo magnetic resonance imaging of diffusion. CR Acad Sci Paris, 1985, 301(15): 1109~1112
[15] Westin CF, Maier SE,Khidir B, et al. Image processing for diffusion tensor magnetic resonance imaging. Lecture notes in Computer Science: Medical image computing and computed-assisted intervention. Cambridge: Springer, 1999: 441~452
[16]刘志远,郑永果,基于VTK的医学图像三维重建,信息技术与信息化,2009,33(1): 59~61
[17] Raymond CM, Ryan O. Fluent VTK Extractor. HPCMP Users Group Conference IEEE, 2006, 384~388
[18] Cheng WL, Chen CX, Qian ZY, et al. Research on medical image three dimensional visualization system. Inter Conf Comp Med Engin IEEE, 2007: 919~923
[19] Ageenko E, Russa GL. A Visualization Toolkit for teaching, learning and experimentation in image processing. Frontiers Educ Conf IEEE, 2005: 21~26
[20] Schroeder WJ. The VTK User’s Guide. New York: Kitware , 1998: 122~157
[21] Liu L, Luo HY, Zhang SX, et al. Zhongguo Yixue Yingxiang Jishu, 2009, 25(1): 145~148
[22]苏金树,刘雪青,UNIX网络性能管理的流量监测技术研究,国防科技大学学报,1998,20(1):15~18
[23] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. Comput Graph Appl IEEE, 2000, 20(5): 20~27
[24] Schroeder WJ, Martin K, Lorensen B. The Visualization Toolkit: An Object Object-Oriented Approach to 3D Graphics. 3rd Edition. New York: Kitware, 2003
[25]欧海峰,基于VTK的医学图像三维重建及其可视化,中国科技信息,2007,19(22): 187~188
[26]廖其光,鲍苏苏,潘家辉等,基于VTK肝脏三维模型可视化研究与实现,计算机与数字工程,2008,36(2):102~104
[27]黄姗姗,王博亮,闵小平,基于VTK的可视化技术的研究,中国数字医学,2008,3(1): 31~34
[28] Dutra M, Rodrigues PS, Giraldi GA. Distributed Visualization using VTK in grid environments. IEEE Inter Symp Cluster Comp Grid, 2007: 973~981
[29] Pierpaoli C. Oh no! one more method for color mapping of fiber tract direction using diffusion MR imaging data. Vancouver, Canada: In ISMRM Proceedings, 1997:1741
[30] Jones D, Williams S, and Horsfield M. Full representation of white-matter fibre direction on one map via diffusion tensor analysis. Vancouver, Canada: InISMRM Proceedings, 1997: 1743
[31] Westin CF, Maier SE, Khidhir B, Everett P, Jolesz AF, and Kikinis R. Image processing for diffusion tensor magnetic resonance imaging. In Proceedings of Second Int. Conf. on Medical Image Computing and Computer-assisted Interventions, 1999: 441~452
[32] Mori S, van Zijl PC. Fiber tracking: principles and strategies-a technical review. NMR in biomedicine, 2002, 15(7-8): 468~680
[33] Law M, Weinstein DM, Tsuruda JS, et al. White matter tractogaphy using diffusion tensor deflection. Human brain mapping, 2003, 18(4): 306~321
[34] Masomi AR, Sirivong B, Konrad J. Multiple motion segmentation with level sets. Proceedings of the SPIE, 2000, 3974: 584~595
[35] Sethian JA. Level Set Methods and Fast Marching Methods: Evolving interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science. New York: Cambridge University Press, 1999
[36]杨新,图像偏微分方程的原理与应用,上海:上海交通大学出版社,2006,20~44
[37] Kindlmann G, Weinstein D, and Hart D. Strategies for direct volume rendering of diffusion tensor fields. IEEE transactions on visualization and computer graphics, 2000, 6(2): 124~138
[38] Mori S, Crain BJ, Chacko VP, et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 1999, 45(2): 265
[39] Parka GJ, Stephen KE, Barker GJ, et al. Initial demonstration of in vivo tracing of axonal projections in the macaque brain and comparison with the human brain using diffusion tensor imaging and fast marching tractoraphy. Neurolmage, 2002, 15(4): 797~809
[40] Chop D, Computing minimal surfaces via Level Set curvature flow. Journal of Computational Physics, 1993, 106:77~91
[41]于春水,李坤成,弥散张量成像纤维跟踪技术的研究进展,中国医学影像技术,2004,20(3): 477~479
[42]赵欣,王明时,高伟等,基于扩散张量的脑白质内神经纤维束的可视化技术,生物医学工程学杂志,2006,23(4):899~902
[43]汤天宇,扩散张量数据处理及脑白质纤维追踪技术:[硕士学位论文],南京:东南大学,2007
[44]陈穗芬,磁共振扩散张量的可视化研究:[硕士学位论文],广州:第一军医大学,2007
[45] Weinstein DM, Kindlmann GL, Lundberg EC. Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: IEEE Visulization Proc,San Francisco, 1999, 249~25
[46]米博会,基于DTI的脑白质神经纤维追踪技术及其应用:[硕士学位论文],天津:天津大学,2008
[47] Staempfli P, Jaermann T, Crelier GR, et al. Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging. NeuroImage, 2006, 30:110~120
[48]杨香香,基于多梯度方向扩散张量成像的脑神经纤维可视化研究:[硕士学位论文],天津:天津大学,2010
[49]诸葛斌,冯焕清,周荷琴,医学图像体绘制中的三线性插值算法,航天医学与医学工程,2003,3(16):206-209
[2] Mukherjee P, Miller JH, Shimony JS, et al. Difusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol, 2002, 23(9): 1445~1456
[3] Wang S, Poptani H, Bilello M, et al. Diffusion tensor imaging in amyotrophic lateral scleorsis: volumetric analysis of the corticospinal tract. AJNR Am J Neuroradiol, 2006, 27(6): 1234~1238
[4] Sinha S, Sinha U. In vivo diffusion tensor imaging of the human prostate. Magnetic resonance in medicine, 2004, 52(3): 530~537
[5] Casanova MF. Neuorpathological and genetic findings in autism: the significance of a putative minicolumnopathy. The Neuroscientist, 2006, 12(5): 435~441
[6] Ashtari M, Kumra S, Bhaskar SL. Attention-deficit hyperactivity disorder: a preliminary diffusion tensor imaging study. Biological psychiatry, 2005, 57(5): 448~455
[7] Kubicki M, Park H, Westin CF, et al. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. Neurolmage, 2005, 26(4): 1109~1118
[8] Basser PJ, Pajevic S, Pierpaolic, et al .In vivo fiber tractography using DT-MRI data. Magnetic resonance in medicine, 2000, 44(4): 625~632
[9] Masutani Y, Aoki S, Abe O, et al. MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. European journal of radiology, 2003, 46(1): 53~66
[10] Sethian, JA. A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci, 1996, 93: 1591~1595
[11] Parker GJ, Wheeler-Kingshott CA, Barker GJ. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE transsctions on medical imaging, 2002, 21(5): 505~512
[12] Kumazawa S, Yoshiura T, Arimura H, et al. White matter fiber tractography based on a directional diffusion field in diffusion tensor MRI. Proceeding of SPIE, 2006, 61(44): 1260~1268
[13] Watts R, Liston C , Niogi S, et al. Fiber tracking using magnetic resonance diffusion tensor imaging and its applications to human brain development. Mental Retardation and Developmental Disabilities Research Reviews, 2003,9(3): 168~ 191
[14] Le BD, Breton E. In vivo magnetic resonance imaging of diffusion. CR Acad Sci Paris, 1985, 301(15): 1109~1112
[15] Westin CF, Maier SE,Khidir B, et al. Image processing for diffusion tensor magnetic resonance imaging. Lecture notes in Computer Science: Medical image computing and computed-assisted intervention. Cambridge: Springer, 1999: 441~452
[16]刘志远,郑永果,基于VTK的医学图像三维重建,信息技术与信息化,2009,33(1): 59~61
[17] Raymond CM, Ryan O. Fluent VTK Extractor. HPCMP Users Group Conference IEEE, 2006, 384~388
[18] Cheng WL, Chen CX, Qian ZY, et al. Research on medical image three dimensional visualization system. Inter Conf Comp Med Engin IEEE, 2007: 919~923
[19] Ageenko E, Russa GL. A Visualization Toolkit for teaching, learning and experimentation in image processing. Frontiers Educ Conf IEEE, 2005: 21~26
[20] Schroeder WJ. The VTK User’s Guide. New York: Kitware , 1998: 122~157
[21] Liu L, Luo HY, Zhang SX, et al. Zhongguo Yixue Yingxiang Jishu, 2009, 25(1): 145~148
[22]苏金树,刘雪青,UNIX网络性能管理的流量监测技术研究,国防科技大学学报,1998,20(1):15~18
[23] Schroeder WJ, Avila LS, Hoffman W. Visualizing with VTK: a tutorial. Comput Graph Appl IEEE, 2000, 20(5): 20~27
[24] Schroeder WJ, Martin K, Lorensen B. The Visualization Toolkit: An Object Object-Oriented Approach to 3D Graphics. 3rd Edition. New York: Kitware, 2003
[25]欧海峰,基于VTK的医学图像三维重建及其可视化,中国科技信息,2007,19(22): 187~188
[26]廖其光,鲍苏苏,潘家辉等,基于VTK肝脏三维模型可视化研究与实现,计算机与数字工程,2008,36(2):102~104
[27]黄姗姗,王博亮,闵小平,基于VTK的可视化技术的研究,中国数字医学,2008,3(1): 31~34
[28] Dutra M, Rodrigues PS, Giraldi GA. Distributed Visualization using VTK in grid environments. IEEE Inter Symp Cluster Comp Grid, 2007: 973~981
[29] Pierpaoli C. Oh no! one more method for color mapping of fiber tract direction using diffusion MR imaging data. Vancouver, Canada: In ISMRM Proceedings, 1997:1741
[30] Jones D, Williams S, and Horsfield M. Full representation of white-matter fibre direction on one map via diffusion tensor analysis. Vancouver, Canada: InISMRM Proceedings, 1997: 1743
[31] Westin CF, Maier SE, Khidhir B, Everett P, Jolesz AF, and Kikinis R. Image processing for diffusion tensor magnetic resonance imaging. In Proceedings of Second Int. Conf. on Medical Image Computing and Computer-assisted Interventions, 1999: 441~452
[32] Mori S, van Zijl PC. Fiber tracking: principles and strategies-a technical review. NMR in biomedicine, 2002, 15(7-8): 468~680
[33] Law M, Weinstein DM, Tsuruda JS, et al. White matter tractogaphy using diffusion tensor deflection. Human brain mapping, 2003, 18(4): 306~321
[34] Masomi AR, Sirivong B, Konrad J. Multiple motion segmentation with level sets. Proceedings of the SPIE, 2000, 3974: 584~595
[35] Sethian JA. Level Set Methods and Fast Marching Methods: Evolving interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science. New York: Cambridge University Press, 1999
[36]杨新,图像偏微分方程的原理与应用,上海:上海交通大学出版社,2006,20~44
[37] Kindlmann G, Weinstein D, and Hart D. Strategies for direct volume rendering of diffusion tensor fields. IEEE transactions on visualization and computer graphics, 2000, 6(2): 124~138
[38] Mori S, Crain BJ, Chacko VP, et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 1999, 45(2): 265
[39] Parka GJ, Stephen KE, Barker GJ, et al. Initial demonstration of in vivo tracing of axonal projections in the macaque brain and comparison with the human brain using diffusion tensor imaging and fast marching tractoraphy. Neurolmage, 2002, 15(4): 797~809
[40] Chop D, Computing minimal surfaces via Level Set curvature flow. Journal of Computational Physics, 1993, 106:77~91
[41]于春水,李坤成,弥散张量成像纤维跟踪技术的研究进展,中国医学影像技术,2004,20(3): 477~479
[42]赵欣,王明时,高伟等,基于扩散张量的脑白质内神经纤维束的可视化技术,生物医学工程学杂志,2006,23(4):899~902
[43]汤天宇,扩散张量数据处理及脑白质纤维追踪技术:[硕士学位论文],南京:东南大学,2007
[44]陈穗芬,磁共振扩散张量的可视化研究:[硕士学位论文],广州:第一军医大学,2007
[45] Weinstein DM, Kindlmann GL, Lundberg EC. Tensorlines: advection-diffusion based propagation through diffusion tensor fields. In: IEEE Visulization Proc,San Francisco, 1999, 249~25
[46]米博会,基于DTI的脑白质神经纤维追踪技术及其应用:[硕士学位论文],天津:天津大学,2008
[47] Staempfli P, Jaermann T, Crelier GR, et al. Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging. NeuroImage, 2006, 30:110~120
[48]杨香香,基于多梯度方向扩散张量成像的脑神经纤维可视化研究:[硕士学位论文],天津:天津大学,2010
[49]诸葛斌,冯焕清,周荷琴,医学图像体绘制中的三线性插值算法,航天医学与医学工程,2003,3(16):206-209