DTI数据分析及应用ppt课件.ppt

DTI数据分析及应用,1,Page 2,内容提纲,DTI的研究内容,DTI数据处理流程,DTI Studio,FSL: FMRIBs Diffusion Toolbox,2,Page 3,扩散张量成像的研究内容,纤维跟踪算法,基于DTI的应用研究,3,Dxyxy Dyy Dyz = (v1 v2 v3) 0 2 0,扩散张量的数学描述,D=,特征分解 特征值: 123 0 特征向量: vivj , i j Page 4,Dxx Dxy Dxz 1 0 0 v1 v2 Dxz Dyz Dzz 0 0 3 v3,4,Page 5,确定性跟踪算法,跟踪终止条件,Mori et al., Ann Neurol, 1999,5,Page 6,确 定 性 跟 踪 结 果,Catani et al, Brain, 2005,粗大的白质纤维束,6,Uncertainty,Page 7,纤维走向的不确定性,Jones, MRM, 2003,Linearity,Bootstrap 方法,7,Page 8,概率跟踪算法,Direction Uncertainty,DTI Noise,Partial Volume Effects,Slide from Tri Ngo,8,Page 9,概率跟踪的方法,Non-parametric (model free) approaches,Bootstrap method,HARDI: Q-ball, DSI,Parametric approaches,Prior knowledge and models: Bayesian framework Probability density function (PDF): local, global,How to estimate the distribution of fiber,orientations within a voxel?,9,Page 10,概率跟踪的思想 Reference: Behrens, T.E. et al. Characterization and propagation of uncertainty,in diffusion-weighted MR imaging. Magn Reson Med 50, 1077-88 (2003).,10,Page 11,概 率 跟 踪 结 果,Friman et al, IEEE TMI,2006,11,Page 12,概率跟踪的优点：,估计纤维走向的不确定性,一定程度上解决纤 维交叉问题,研究FA较低的灰质脑区之间的解剖连接 跟踪结果对噪声更稳定,定量描述空间任意两个体素之间的连接概率,概率跟踪的缺点：,需要采集较多梯度方向的DTI图像 计算量大，耗时,12,Page 13,Connectivity-based classification of thalamic voxels produces clusters,Behrens et al, Nature Neuroscience, 2003,13,Page 14,Improvements on the diffusion tensor model,single fibre,multiple fibres,Slide from Saad Jbabdi,14,Page 15,确定性跟踪常用软件:,DTI Studio, MedINRIA, 3D Slicer等,概率跟踪常用软件：,FSL,http:/www.fmrib.ox.ac.uk/fsl/fdt/,15,Page 16,扩散张量成像的研究内容,纤维跟踪算法,基于DTI的应用研究,16,Page 17,扩散属性测度,以上三种情况的 ADC = 0.7 x 10-3 mm2/s,17,Page 18,18,Page 19,19,Page 20,20,Page 21,基于扩散属性测度的临床研究,基于全脑配准的分析方法,基于体素的统计分析（VBA）,基于白质骨架的空间统计分析（TBSS）,基于感兴趣区的分析方法,手工画感兴趣区的方法 基于纤维重建的定量分析,21,Page 22,Voxel-Based Analysis (VBA),VBM on FA (Ashburner, 2000; Rugg-Gunn, 2001) Strengths,Fully automated & quick Investigation whole brain,Implementation steps,Preprocessing,Normalization of FA images Smooth,Voxelwise statistics (e.g. controls patients),Issues,Alignment difficult; smallest systematic shifts between groups can be incorrectly interpreted as FA change,No objective way to choose smoothing extent (6, 8 or 10 mm?),22,Page 23,23,Page 24,24,Page 25,25,Page 26,26,Page 27,27,Page 28,28,Page 29,29,Page 30,30,精神分裂症患者的VBA分析 FA降低的脑区：, ,cerebral peduncle; frontal regions; inferior temporal gyrus; medial parietal lobes; hippocampal gyrus; Insula; right anterior cingulum bundle; right corona radiata Page 31,Hao et al. Neuroreport 2006,31,Page 32,Tract-Based Spatial Statistics,(TBSS),Part of FSL software,(http:/www.fmrib.ox.ac.uk/fsl/tbss/index.html),Overcome the drawbacks in VBA method, such as alignment issue and smoothing issue Flowchart,32,Page 33,TBSS steps in detail:,preprocessing - create FA images from your diffusion study data,tbss_1_preproc - prepare your FA data in your TBSS working directory in the right format,tbss_2_reg - apply nonlinear registration of all FA images into standard space,tbss_3_postreg - create the mean FA image and skeletonise it,tbss_4_prestats - project all subjects FA data onto the mean FA skeleton,stats (e.g., randomise) - feed the 4D projected FA data into GLM modelling and thresholding in order to find voxels which correlate with your model.,33,Page 34,Do cross-subject voxelwise stats on,skeleton-projected FA,34,Page 35,Fig. TBSS results from 15 MS patients. A,B: 3D surface renderings of the mean FA skeleton. C: Yellow shows the where FA correlates negatively with EDSS disability score. D: Red as above. In C and D, green shows the mean FA skeleton, blue shows the group mean lesion distribution, and the background image is the MNI152.,Smith et al., NeuroImage, 2006,35,Page 36,Scholz et al., Nature Neuroscience 2009,36,Page 37,TBSS data acquisition requirement: Voxel size should be less than 3 3 3 mm3.,At least one b = 0 should be acquired; ideally one b = 0 image for every eight diffusion- weighted images.,b-value should be at least 800 s/mm2.,At least six-gradient directions must be acquired. it is better to use more unique sampling directions (with isotropic angular density18) than to obtain repeat samples of the same set of directions.,SNR in the diffusion-weighted images should be maximized . An example protocol that should lead to,sufficiently high SNR is having b =1,000 s mm2, 24,diffusion-weighted images and SNR greater than 15 in the b = 0 image.,37,Page 38,The data should not be upsampled (e.g., through unfiltered zero-padding during reconstruction) if this is done in such a way as to introduce ringing into the data.,If multiple repeats of b = 0 or diffusion-weighted images are to be acquired, they must not be averaged on the scanner (as theymust be coregistered before averaging, and any risk of averaging the complex data should be avoided).,Fat saturation should be used whenever possible to remove signal from the scalp, which can disrupt signal in the brain owing to chemical shift or ghosting artifacts.,A vitamin capsule leftright marker (oil, not water) should be attached to the right side of the head to avoid any leftright ambiguities during data conversion and analysis.,38,Page 39,Computing equipment:,Unix-based computers. AppleMac (running Mac OS X version 10.4 or higher) and PCs (running Linux flavors RedHat 9, Enterprise, FC4, Suse 9.0-9.3 or Debian),High RAM requirements (particularly if tens of subjects are used in a study), it is likely that the,computer will need to be 64 bit. The computer should have at least a 1 GHz CPU clock, 1 GB RAM, 5 GB swap and 20 GB free hard disk space.,If multiple networked computers (or a computer cluster) are available, the registration steps can be parallelized, greatly reducing the total computation time.,39,Page 40,白质纤维束的定量分析,FA: Left Cingulum (Red) Right Cingulum (Blue),Parameterization process,Gong et al, Human Brain Mapping, 2005,40,Page 41,同正常人相比，早期盲人的视放射白质扩散异常，FA值显著,降低，ADC和23显著升高。,早期盲人大脑白质扩散异常研究,Shu et al, Human Brain Mapping, 2009,41,Page 42,单张量模型的假设无法解决纤维交叉问题,纤维跟踪技术的准确性缺乏严格的评价体系,扩散张量成像的局限性,42,Page 43,内容提纲,DTI的研究内容,DTI数据处理流程,DTI Studio,FSL: FMRIBs Diffusion Toolbox,43,Page 44,数据处理的 基本流程,44,Page 45,DWI from Scanner S0 S1 S2 S3 S4 S5,S6,45,Page 46,Preprocessing,DICOM data conversion,Image quality check,Eddy current correction,46,Page 47,内容提纲,DTI的研究内容,DTI数据处理流程,DTI Studio,FSL: FMRIBs Diffusion Toolbox,47,Page 48,DTI Studio,/ Download & Install User Manual Mailing list,48,Page 49,Launching the Program and Hardware Requirement,DtiStudio-latest-x86.exe for Windows system,More than 1GB RAM is recommended,49,Page 50,Main Functions,Image Viewer,Diffusion Tensor Calculation Fiber Tracking and Editing 3D Visualization,Image File Management,ROI Drawing and Statistics,50,Page 51,How to do tensor calculation,and fiber tracking?,51,Page 52,E:workTrainingExampleData,Raw data: MRIcroN dcm2nii.exe,(.img, .hdr, .bvec, .bval),Eddy current correction: AIR,Tensor, FA, MD calculation: DTIstudio Fiber tractography: DTIstudio ROI selection,52,Page 53,第一步：对原始DICOM数据进行格式转换。利用MRIcroN软件 中的dcm2nii.exe工具，将DTI原始数据文件夹拖入，即可得到 DTI扫描的梯度编码文件.bval和.bvec，以及转换后的NIFTI格式 的图像文件（Output Format选择4D NIfTI hdr/img）。,53,Page 54,第二步：对DTI图像进行头动和涡流校正。打开DTI studio, File - MRI View3D, 选中上一步得到的4D .img文件，Image Parameters中选择Image File Format为 Analyze，点击OK，然后在Image面板Image Processing区域选择Automatic Image Registration (AIR)，按图3进行设置，然后点击OK，等图像配准完成后， 在Image面板的Orthogonal Views区域的文件下拉框中看到Air_开头的一系列文 件，为校正后的DTI图像文件，点击Save，将Air_开头的所有文件选中，选择 Raw Data，保存为一个4D的.dat文件。,MRI View,3D参数,54,Page 55,AIR的参数 设置,55,Page 56,头动和涡流校正后的DTI图像保存,56,Page 57,第三步：张量解算以及FA, ADC等扩散指标的计算。打开DTI studio, File - DTI Mapping, 选择Philips REC格式，Continue，按图5进行参数 设置， Add a file中选中上一步保存的4D .dat文件，点击OK，在,DtiMap面板的Calculation区域选中Tensor, Color Map etc.（计算ADC值 选择ADC-Map），根据图像选择噪声水平，点击OK，然后等DTI Studio算完后在Image面板的Orthogonal View区域可看到计算出来的各 种扩散属性文件。对于想要保存的文件，如FA, EigenVector-0，Color Map-0等可以分别进行Save（.dat格式），便于下一次查看和使用。,57,Page 58,DtiMap面板进行张量解算,58,Page 59,各种扩散属性的显示,59,Page 60,第四步：纤维跟踪及可视化。第一种方法：基于前面步骤，在DtiMap面板的 Fiber Tracking区域点击Fiber Tracking，然后进行参数设置，点击OK，就会进 行基于全脑体素（FA0.2）的纤维重建；第二种方法：如果上一步已保存FA和 Eigen Vector-0文件，可重新打开DTI Studio, File-Fiber-Tracking，选上FA- Map文件和Principle Vector文件，并进行参数设置，点击OK，就会进行基于全 脑体素（FA0.2）的纤维重建。通过任何一种方法，算完后右下角会出现Fiber 面板，再此面板中可以对特定纤维束进行显示和编辑，并可以对纤维属性进行统 计分析。,60,Page 61,纤维跟踪方法2,61,Page 62,纤维跟踪的参数设置,62,Page 63,重建纤维束的可视化,63,Page 64,64,Page 65,Major white matter tracts,Reference: Wakana S, Caprihan A, Panzenboeck MM, et al., Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage, 2007, 36(3): 630-644.,65,Page 66,纤维属性 的统计分 析,66,Page 67,New Modules,ROIEditor,ROI drawi