磁共振扩散张量图像配准的研究
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摘要
近年来,现代大型医疗设备成像技术有了飞速发展,新的成像方法不断涌现。例如,扩散张量成像(Diffusion Tensor Imaging,DTI)技术在磁共振技术的基础上,通过对双极磁场梯度脉冲来对水分子的扩散运动效应进行编码,得到了扩散加权磁共振图像(Diffusion Weighted Image,DWI)。从多个方向(至少6个方向)获取扩散加权图像,得到每一个像素的扩散张量(Diffusion Tensor,DT)的成像过程,称为扩散张量成像。
作为一种非标量医学图像,DT图像利用水分子扩散运动的各向异性进行成像,它能够反映活体组织的空间组成信息及病理状态下各组织之间水分子的交换状况。大脑灰质区和白质区的水分子扩散分别表现出显著的各向同性和各向异性,因而DTI技术在显示白质神经纤维和功能束的走行方向以及三维形态等方面具有极大的优越性,从而对研究脑解剖与诊断白质病变具有重要的意义。作为非侵入性的分析大脑内部结构的重要工具,DTI已经在神经生理学、神经解剖学、神经外科学以及浮肿、多发性硬化症和其他一些脑部肿瘤的研究和诊断中发挥了重要的作用。
医学图像配准是指对不同时间、不同视场、不同成像模式的两幅或多幅图像进行空间几何变换,以使代表相同解剖结构的体素或像素在空间上能够对应起来。众所周知,标量图像配准是一项成熟的图像处理技术,关于标量图像配准的算法也日臻完善,然而由于张量图像每一个像素都对应于一个二阶张量,数据复杂,使得配准技术在DT图像方面的应用屈指可数。之所以造成这种现象,不仅是由于用于标量图像配准的相似性测度不能简单地直接应用于张量图像配准,更主要的是因为,就张量图像而言,图像数据所包含的方向信息和结构信息,与成像组织的解剖结构密切相连,要确保两者之间的一致性,对张量图像进行空间变换后还必须对每个像素的张量进行必要的空间变换,而这是标量图像配准所不需的。
本文介绍了DTI的基本原理、DT及其各向异性测度的定义及计算方法,阐述了DT图像配准方法的分类,重点介绍了张量重定向的原理及方法,分析了近年来扩散张量图像配准的相似性测度和优化方法。
DT图像配准是医学图像配准的一个崭新的领域,不能简单的套用常规的标量图像配准方法,本文主要做了以下3个方面的工作:
1、实现了从DICOM数据采集到DT及其各向异性测度的计算。
2、提出了一种快速DT图像配准方法:基于FA(Fractional Anisotropy)的扩散张量图像配准。虽然DT图像的每一个像素都是一个二阶张量,但是扩散张量的各向异性测度(anisotropy)图像却是标量图像,从而可以借助这些标量图像的配准来实现DT图像的空间变换及图像中每一个像素的张量重定向,从而实现DT图像配准。这种方法计算简单迅速,易于实现,其缺点是未能充分利用张量数据中包含的丰富的方向信息,配准后张量方向信息部分丢失,适用于对计算速度要求较高,而对于精度要求较低的应用。
3、提出了一种显式优化张量重定向的DT图像配准方法,此方法充分考虑到张量数据的特点和物理意义,将张量方向信息融入目标函数,通过显式优化张量重定向从而对张量图像进行配准,在求解时变形场时借助于混合优化算法,成功解决了局部极值的困扰。实验结果表明,该算法稳定性良好,在对扩散张量图像进行配准的同时,能有效保持扩散张量主特征方向与纤维走向的一致性,是一种实用的扩散张量图像配准方法。
In recent years,modern medical imaging devices have been developing quickly, and new imaging technique has kept coming forth.A new Magnetic Resonance imaging(MRI) method called diffusion tensor imaging(DTI) has emerged,which utilizes the diffusion tensor of each voxel calculated from multiple direction diffusion weighted image(DWI)(at least 6 directions).
As a new technology which can reflect the direction of molecule diffusion,DTI can show the information of the structure of tissues and the exchanges of water molecule with each tissue in pathology.It has great advantages on showing the distribution of white matter fiber pathway and its three-dimensional structures because of the distinct anisotropy of the diffusion in water molecule of the brain white matter.Without any other methods can measure the character of the live white matter presently,DTI has great significance for the study of the anatomization of the brain and the diagnosis of the diseases of white matter.As a non-invasive and very important tool for study of the internal structure of the brain,DTI has been widely used in the research of neurophysiology,neurosurgery,and diagnosis of brain cancer, etc.All those function are fully dependent on accurate image registration.
Given two image sets acquired from different patient or the same patient but at different times or with different devices,image registration is the process of finding the geometric transformation that aligns one image to another.As is well known, image registration is a sophisticated image processing technology and algorithms about image registration are also getting more perfect.A lot of work has been done about registration of traditional 2-D and 3-D images,whose values are scalars magnitudes.However,only few papers have been yet published about tensor registration(registration of diffusion tensor images is a problem that has received much less attention.).Unlike conventional images,DTI not only measures the intensity at each voxel,but also the orientation.The main difference between scalar and tensor registration is that in the latter case,not only a spatial transformation must be computed,but also another one must be applied to tensor values in every image voxel.This is the main reason why those scalar registration algorithms can not be directly applied to tensor images.
This paper introduces the basic theory and concept of DTI and its anisotropy measures.It reviews the registration categories.The principal and methods of tensor reorientation is described in detail.It also analyzes the similarities and optimizations of DT images.
Registration of diffusion tensor images is a totally new field,and we can not apply methods of scalar registration directly.Our works are mainly described as follows:
1、Acquisition of DICOM images,calculating diffusion tensor images and their anisotropy images,such as FA images and Trace images.
2、A fast registration method is proposed,which is circumvents tensor reorientation by registering scalar images derived from DT images,thus discarding the orientation component of the data.Although every pixel of diffusion tensor images is a Second-order tensor,their anisotropy images are scalar.Therefore transformation and reorientation of diffusion tensor images can be achieved by registration of those anisotropy images.This method is simple,rapid and easy to achieve,but fails to take full advantage of the abundant direction information and parts of which is lost.It is suitable for registration application which requires for higher computing speed and lower level of accuracy.
3、A novel registration method which performs tensor reorientation by incorporating tensor reorientation in its cost function iteratively is proposed.Taking full account of the characteristic and physical meaning of tensors,information about tensor orientation is merged into objection function and then tensor registration can be achieved simultaneously with tensor reorientation.The final optimization seeks for the global hybrid optimization method,avoiding local extrema successfully.Results shows that this method is a stable one and can keep the principle direction of tensor consistent with fiber orientation effectively.It's really a practical method for tensor registration.
作为一种非标量医学图像,DT图像利用水分子扩散运动的各向异性进行成像,它能够反映活体组织的空间组成信息及病理状态下各组织之间水分子的交换状况。大脑灰质区和白质区的水分子扩散分别表现出显著的各向同性和各向异性,因而DTI技术在显示白质神经纤维和功能束的走行方向以及三维形态等方面具有极大的优越性,从而对研究脑解剖与诊断白质病变具有重要的意义。作为非侵入性的分析大脑内部结构的重要工具,DTI已经在神经生理学、神经解剖学、神经外科学以及浮肿、多发性硬化症和其他一些脑部肿瘤的研究和诊断中发挥了重要的作用。
医学图像配准是指对不同时间、不同视场、不同成像模式的两幅或多幅图像进行空间几何变换,以使代表相同解剖结构的体素或像素在空间上能够对应起来。众所周知,标量图像配准是一项成熟的图像处理技术,关于标量图像配准的算法也日臻完善,然而由于张量图像每一个像素都对应于一个二阶张量,数据复杂,使得配准技术在DT图像方面的应用屈指可数。之所以造成这种现象,不仅是由于用于标量图像配准的相似性测度不能简单地直接应用于张量图像配准,更主要的是因为,就张量图像而言,图像数据所包含的方向信息和结构信息,与成像组织的解剖结构密切相连,要确保两者之间的一致性,对张量图像进行空间变换后还必须对每个像素的张量进行必要的空间变换,而这是标量图像配准所不需的。
本文介绍了DTI的基本原理、DT及其各向异性测度的定义及计算方法,阐述了DT图像配准方法的分类,重点介绍了张量重定向的原理及方法,分析了近年来扩散张量图像配准的相似性测度和优化方法。
DT图像配准是医学图像配准的一个崭新的领域,不能简单的套用常规的标量图像配准方法,本文主要做了以下3个方面的工作:
1、实现了从DICOM数据采集到DT及其各向异性测度的计算。
2、提出了一种快速DT图像配准方法:基于FA(Fractional Anisotropy)的扩散张量图像配准。虽然DT图像的每一个像素都是一个二阶张量,但是扩散张量的各向异性测度(anisotropy)图像却是标量图像,从而可以借助这些标量图像的配准来实现DT图像的空间变换及图像中每一个像素的张量重定向,从而实现DT图像配准。这种方法计算简单迅速,易于实现,其缺点是未能充分利用张量数据中包含的丰富的方向信息,配准后张量方向信息部分丢失,适用于对计算速度要求较高,而对于精度要求较低的应用。
3、提出了一种显式优化张量重定向的DT图像配准方法,此方法充分考虑到张量数据的特点和物理意义,将张量方向信息融入目标函数,通过显式优化张量重定向从而对张量图像进行配准,在求解时变形场时借助于混合优化算法,成功解决了局部极值的困扰。实验结果表明,该算法稳定性良好,在对扩散张量图像进行配准的同时,能有效保持扩散张量主特征方向与纤维走向的一致性,是一种实用的扩散张量图像配准方法。
In recent years,modern medical imaging devices have been developing quickly, and new imaging technique has kept coming forth.A new Magnetic Resonance imaging(MRI) method called diffusion tensor imaging(DTI) has emerged,which utilizes the diffusion tensor of each voxel calculated from multiple direction diffusion weighted image(DWI)(at least 6 directions).
As a new technology which can reflect the direction of molecule diffusion,DTI can show the information of the structure of tissues and the exchanges of water molecule with each tissue in pathology.It has great advantages on showing the distribution of white matter fiber pathway and its three-dimensional structures because of the distinct anisotropy of the diffusion in water molecule of the brain white matter.Without any other methods can measure the character of the live white matter presently,DTI has great significance for the study of the anatomization of the brain and the diagnosis of the diseases of white matter.As a non-invasive and very important tool for study of the internal structure of the brain,DTI has been widely used in the research of neurophysiology,neurosurgery,and diagnosis of brain cancer, etc.All those function are fully dependent on accurate image registration.
Given two image sets acquired from different patient or the same patient but at different times or with different devices,image registration is the process of finding the geometric transformation that aligns one image to another.As is well known, image registration is a sophisticated image processing technology and algorithms about image registration are also getting more perfect.A lot of work has been done about registration of traditional 2-D and 3-D images,whose values are scalars magnitudes.However,only few papers have been yet published about tensor registration(registration of diffusion tensor images is a problem that has received much less attention.).Unlike conventional images,DTI not only measures the intensity at each voxel,but also the orientation.The main difference between scalar and tensor registration is that in the latter case,not only a spatial transformation must be computed,but also another one must be applied to tensor values in every image voxel.This is the main reason why those scalar registration algorithms can not be directly applied to tensor images.
This paper introduces the basic theory and concept of DTI and its anisotropy measures.It reviews the registration categories.The principal and methods of tensor reorientation is described in detail.It also analyzes the similarities and optimizations of DT images.
Registration of diffusion tensor images is a totally new field,and we can not apply methods of scalar registration directly.Our works are mainly described as follows:
1、Acquisition of DICOM images,calculating diffusion tensor images and their anisotropy images,such as FA images and Trace images.
2、A fast registration method is proposed,which is circumvents tensor reorientation by registering scalar images derived from DT images,thus discarding the orientation component of the data.Although every pixel of diffusion tensor images is a Second-order tensor,their anisotropy images are scalar.Therefore transformation and reorientation of diffusion tensor images can be achieved by registration of those anisotropy images.This method is simple,rapid and easy to achieve,but fails to take full advantage of the abundant direction information and parts of which is lost.It is suitable for registration application which requires for higher computing speed and lower level of accuracy.
3、A novel registration method which performs tensor reorientation by incorporating tensor reorientation in its cost function iteratively is proposed.Taking full account of the characteristic and physical meaning of tensors,information about tensor orientation is merged into objection function and then tensor registration can be achieved simultaneously with tensor reorientation.The final optimization seeks for the global hybrid optimization method,avoiding local extrema successfully.Results shows that this method is a stable one and can keep the principle direction of tensor consistent with fiber orientation effectively.It's really a practical method for tensor registration.
引文
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[2]PJ.Basser,J.Mattiello,D.Le Bihan.Estimation of the effective self-diffusion tenser from the NMR spin echo[J].J Magn Reson.1994,B103:247-254.
[3]P.J.Basser,Mattiello,D.Le Bihan.MR diffusion tensor spectroscopy and imaging[J].Biophys J 1994,66:259-0267.
[4]J.Mattiello,P.J.Basser,D.Le Bihan.Analytical expressions for the b matrix in MR diffusion imaging and spectroscopy[J].J Magn Reson,1994,108:131-141.
[5]P.J.Basser,J.Mattiello,D.Le Bihan.MR imaging of fiber-tract direction and diffusion in anisotropic tissues[J].Abstracts of the proceedings of the 20th annual meeting of the Society of MRM,New York,1993,585.
[6]Zitova B.,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,21:977-1000
[7]Kyriakopoulos M,Bargiotas T,Barker GJ et al.Diffusion tensor imaging in schizophrenia.Eur Psychiatry,Jun 2008,23(4):255-73.
[8]D.J.Tisserand,G.Stanisz,N.Lobaugh,et al.Levine.Diffusion tensor imaging for the evaluation of white matter pathology in traumatic brain injury.Brain Cogn,Mar 2006,vol.60,no.2,pp.216-217.
[9]N.Makris,A.J Worth,G.Sorensen,et al.,Morphometry of in vivo human white matter association pathways with diffusion weighted MRI,Annals of Neurology,1997,42(6):951-962.
[10]Susumu Moil and Peter B.Barker,Diffusion magnetic resonance imaging:Its principle and applications.The Anatomical Record,1999,257(3):102-109.
[11]D C Alexander and J C Gee.Elastic matching of diffusion tensor images.Comput.Vis.Image Understand,2000,77(2):233-250.
[12]J Ruiz-Alzola,C-F Westin,S K Warfield,et al.Nonrigid registration of 3d tensor medical data.Medical Image Analysis,2002,6:143-161.
[13]A Guimond,C R G Guttmann,S K Warfield,et al.Deformable registration of DT-MRI data based on transformation invariant tensor characterisitics.In IEEE International Symposium on Biomedical Imaging:Macro to Nano,July 2002,pages 761-764.
[14]D K Jones,L D Griffin,D C Alexander,et al.Spatial normalization and averaging of diffusion tensor MRI data sets.NeuroImage,2002,17:592-617.
[15]H-J Park,M Kubicki,M E Shenton,et al.Spatial normalization of diffusion tensor MRI using multiple channels.NeuroImage,2003,20:1995-2009.
[16]K M Curran and D C Alexander.Diffusion tensor orientation matching for image registration.In SPIE Medical Imaging,volume 5032,May 2003,pages 149-156.
[17]K M Curran and D C Alexander.Orientation coherence optimisation in tensor image registration.In Proc.MIUA,2004.
[18]Stejskal EO and Tanner JE.Spin diffusion measurements:spin echoes in the presence of a time dependent field gradient.Chem Phys,1965,42:288-291.
[19]Mattiello J,Basser PJ,and Le Bihan D.The b matrix in diffusion tensor echo-planar imaging.Magn Reson Med,1997,37:292-300.
[20]Basser PJ and Pierpaoli C.A simplified method to measure the diffusion tensor from seven MR images.Magn Reson Med,1998,39:928-934.
[21]Cooper RL,Chang DB,Young AC,et al.Restricted diffusion in biophysical systems.Biophysical Journal,1974,14:161-177.
[22]Basser PJ.Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.J Magn Reson B,1996,111:209-219.
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