使用fsl进行MRI脑图像分析，安装教程，fsl Course，bet去除颅骨，fslroi选取感兴

文件名称: 使用fsl进行MRI脑图像分析

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上传时间: 2019-10-09

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详细说明：使用fsl进行MRI脑图像分析，安装教程，fsl Course，bet去除颅骨，fslroi选取感兴趣区域，FAST分割+偏置场校正，Partial Volume Segmentation 图像分割结果，fslstats 统计，FIRST 皮层下结构分割及统计分析，Vertex Analysis，Volumetric Analysis，信息汇总。 o FSLeyes FSLeyes File overlay View Settings Tools structura Brightness 三是R=F Greyscale 回回回目回目+Q oeo wangwang-VirtualBox -/Fsl/seg_struc/fast structural brain TT rot nti. gz sub2 tI. ntL. gz s wang wang- virtualBox in /fsl/seg_struc/fast [10: 39: 86] totaL 18M drwxr-xr-x 2 wang wang 4.OK 310:37 drwxr-xr-x 9 wang wang 4. OK 362019 .rwxr-xr-x 1 wangwang 2.IN 21 2017 structuraL brain 7T. ntL. gz rwxr-xr-x 1 wang wang 125K 21 2017 structuraL brain 7T rot nt1 9z -rwxr-xr-x 1 wang wang 13M 21 2017 structuraL ntt rwxr-xr-x 1 wang wang 1. 7M 21 2017 sub2_t1.nit. gz rwxr-xr-x 1 wang wang 1. 7M 7 21 2017 sub2_t2.nit.gz wang a wang virtuaLBox in -/fsL/seg struc/fast [18: 39: 88] s bet structural nit structural_brain wang a wang- Vir tua LBox in -/fsl/seg_struc/fast [10: 39: 30] otal 20M drwxr→xr-x2 wang wang drwxr-xr-x 9 wangwang 4. OK 32619 rwxr-xr-x 1 wangwang 2. 1M 21 2017 structuraL brain 7T. nit. 92 rwxr-xr-x 1 wang wang 125K 21 2017 structural brain_7T- rot ntt. gz fw·rw·『 ang wang 2. 2M 30 10: 39 structuraL brain.ntt.9z rwx.xr-x 1 wang wang 13M 21 2017 structuraL ntt rwxr-xr-x 1 wang wang 1. 7M 21 2017 sub2_tinti.gz rwxr-xr-x 1 wangwang 1. 7M 1 2017 sub2 t2 nii. gz Overlay list 去除颅骨后的大脑图层 wang wang- Virtua LBox in -/fsl/seg_struc/fast [10: 39: 32] structural brain s fsleyes structural, nii structural brain strucural 整个大脑的图层(含颅骨) wang g wang- virtua LBox in -/fsl/seg_struc/fast [10: 40: 28]C: 2 gunzip structural-bralnnttgz wang a wang-virtualBox in -/fsl/seg_ struc/fast [10: 40: 38] 22 fslr选取感兴趣区域也可以使用slri选取感兴趣的区域,再加载进 Emsley fslroi structural brain structural brain roi 0 175 0 185 100 5 o。 O FSLe FSLeyes File overlay View Settings Tools Brightness. a|l回回目巴Q— Overlay list Locat on Coordinates: Scanner anatomical Voxel locati structural brain rol structural brain roi 7653788 82 82812640 2189268 structural 同02 18281102}64 volume 23FAST分割+偏置场校正示例中还有输入Fastωu命令,进行偏置场校正及分割,设置同官网,进行校正。输出3类,输出选项全都勾上,然后 Iteration共10次 Use the GUI(Fast [or Fast _gui on a Mac])and turn on the Estimated bias fieldbutton(which saves a copy of the bias field) and Restored input button(which corrects the original image with the calculated bias field ). For both images also open the Advanced Options tab and change the Number of iterations for bias field removal to 10 to account for the strong bias fields in both cases 此时并没有配准到MN152的空间关于为何选择输出3类: Now choose the Number of classes to be segmented. Normally you will want3( Grey Matter, White Matter and CSF). However, if there is very poor grey/ white contrast you may want to reduce this to 2; alternatively, if there are strong lesions showing up as a fourth class, you may want to increase this. Also, if you are segmenting T2-weighted images, you may need to select 4 classes so that dark non-brain matter is processed correctly(this is not a problem with T1-weighted as CSF and dark non-brain matter look similar Ps:下图中选项中是选择6类,注意改成3类 060 FAST- FMRIB's Automated Segmentation Tool-V ● o Fast GUI Input structural brain roi_pve 2-cm red-yellow -dr 0.5 1& mber of input channels1彐 [1]5610 Input image/home/wang/Fsl/seg_struc/f ast/structural el wang wang-virtua LBox in "/fsl/seg_struc/fast [15: 38: 02 s Gtk-Message: 15: 38: 03, 356: Failed to load module "over lay-scrollbar Image type T1-weighted Gtk-Message: 15: 38:. 358: Failed to load module "atk-bridge' utput Gtk-Message: 15: 38: 03, 358: Failed to load module"unity-gtk-module Gtk-Message: 15: 38: 03. 361: Failed to Load module "canberra-gtk-module" Output image(s)basename/home/wang/Fsl/seg_struc/fast/structural. unrecognized arguments: structural brain roi restore nii. gz structural br pve o-cm green -dr 0.5 1 structural brain roi pve 1-cm blue-lightblue 卜 lumber of classes6 1 structural brain roi pve 2-cm red-yellow -dr 0.5 1 Output images: Binary segmentation: Also output one image per class FSLeves version 0.30. 1 Partial volume maps Restored input Estimated Bias field Usage: fsleyes [options] file [displayopts] file [displayopts] v Advanced options Advanced 1]+5616exit1 fsleyes structural brain roi restore nii. gz struc Main MRF parameter 0. 1 e atn roL_pve-c門 Number of iterations for bias field removal 10 e wang wang-Virtua LBox in "/fsl/seg_ struc/fast [15: 38: 03] Fast guI Bias field smoothing (FWHM in mm)20. 04 Use a-priori probability maps for initialisation u Standard to Input FLIRT transfor m//usr/local/fsl/etc/flirtsch/ida Use file of initial tissue-type means G XI 24 Partial Volume Segmentation图像分割结果 fsleyes structural brain roi restore structural brain roi pve 0-cm green -dr 0.5 1 structural brain roi pve 1 -cm blue-lightblue -dr 0.5 1 structural brain roi_pve_2-cm red-yellow -dr 0.5 1& 下图就是图像分割结果,不同颜色分别代表GM,WM,CSF;灰质,白质,脑脊液。脑脊液(绿色pVe0) 灰质(蓝色pve1) ·白质(红黄pve2) 000 FSLeyes FSLeyes File Overlay View Settings Tools oO Structurs_brain_oi pve Opacity Brightness IContrast 厂== Red-Yellow B回目目+Q ↓ oeo wang- VirtualBox:-/fsl/seg_struc/fast structural brain roi nii structuraL brain rot seg. nit. gz t pve 0.ntt,g structural, ni ucturaL brain rot pve 1.ntt.g structural brain rot pve 2.ntt. gz .ntL.9 wang g wang- lBox in -/fsl/seg_struc/fast [15: 32: 33] s fsleyes structural brain rot restore ntt. g: structural brain roi pve o-cm green dr 0.51 structural brain roi_ pve 1-cm bluelightblue tructural brain rot pve 2-cm red-yellow -dr 0.5 1 [1]5496 wang e wang- Virtua LBox in - /fsl/seg_struc/fast [15: 32: 37] OveRlay list Gtk-Message: 15 38. 771: Failed to load module overlay-scrollbar Gtk-Message: 15:32:38.772: Falled to load module"atk-brtdge 巴 structural brain rol pve2 tk.Message: 15:32:38 Failed to load module "unity.gtkmodule a Structural brain rolpve. 2 eloo structural, brain_rol_pve_1 Gtk-Message: 15: 32: 38.776: Failed to load module "canberra-gtk-module WARNING annotations, py 254: draw Attempt to call an undefi structuralbrain_roi_pve_1 图 o structural_brain_rol_pve_o ned function glutstrokewtdth, check for bool(gLutstrokewidth) before calling raceback (most recent call last): structural brain_ roi pve_0 File"/usr/local/fsl/fslpython/envs/fslpython/lib/python. 7/site-packages/fsl 79912]:00 structural brain ro restoro eyes/gU/annotations. py", line 251, in draw 79912}13335842895507812 各种组织分割的结果如下 *pVe_*文件表示每类的概率,没有进行二值化 *seg*文件表示每类分割结果,进行过二值化 bias文作为偏置场。 25 fsIstats统计第一个数字表示整个图像上GM(pve1是灰质)的平均体素,就是个百分比,第二个是整张图体素数日,第三个数字是图像的总体积(单位:立方毫米),忽略了所有的零体素(就是脑袋外的体素不算在内),将第一和第三个数字相乘可以得到GM的总体积。 wang a wang- Vir tua LBox in w/fsl/seg struc/fast [16: 19: 11] fslstats structural brain pve 1 -M-V .676181351749351749.0000 wang g wang-Virtua LBox in -/fsl/seg_struc/fast [16: 19: 22 fslstats structural brain pve 2-M-V 710148423795423795.000000 ang a wang-virtualBox in "/fsl/seg. struc/fast [16: 20: 33] fslstats structural brain pve 3 683672406342466342.000000 wang wang-virtuaLBox in "/fsl/seg_struc/fast [16: 21: 29] fslstats structural brain pve 6-M-V .723275222486222480.00000 wang a wang-VirtuaLBox in */fsl/seg_struc/fast [16: 21: 34] fslstats structural brain pve 69790143411434011.000009 wang g wang-VirtuaLBox in -/fsl/seg_struc/fast [16: 21: 40] fslstats structural brain pve 5-M-V 70133130905360905.000000 26FRST皮层下结构分割及统计分析首先,进入~/fs1/ seg struc/ first目录 run first all -i con0047 brain -b-s L_Hipp, L Amyg -o con 0047 -a con0047 brain to std sub. mat -a指定了仿射配准的短阵,这个步骤也可以自动完成-s指定要分割的部分,此处是 L Hipp,L_Amyg 将图像 congo47 brain to std sub,ni.gz与“1mm标准空间模板图像一起加载到 FSLeyes中。查看皮层下结构的对齐方式两者都是MN152 space,所以非常接近。 FSLeyes File Overlay view SettingsTools cno047 brain ortho view1 Take screenshot Crewel Save animated GIF show command line for scene Apply command line arguments Alt+N Link display settin Alt+s Reset display Alt+R Centre cursor Alt+P Centre cursor at(0, 0, 0) Alt+o Show/hide labels Alt+L v show/hide location cursor Alt+C show/hide x (sagittal) canvas Alt+x Y show/hide Y(coronal) canvas Alt+Y Show/hide Z (axial) ca Alt+z 4 Overlay list Ctrl+Alt+1 Location pane Ctrl+Alt+2 Overlay information Ctrl+Alt+3 Overlay display panel Ctrl+Alt+4 View settings p Ctrl+Alt+5 ˇ Atlas panel ctrl+Alt+6 v Ortho toolbar Ctrl+Alt+8 Lookup tables p cluster browser Melodic iC classification Remove all panel Ctrl+Alt+x Close ctrl+v Location TAtas in formation Atlas search Atlas management Coordinates: Scanner anatomical voxel locati Bloo mnvlabevall 相10.625062 joocon0o47-brain, to_std_sub OMars Parietal connec.y-based parcellation The selected overlay does not appear to be in B Mars TPJ connectiVity based parcellation MNI152 space atlas in Formation might not 30.63761 n00d7 brain E MN Structural Atlas be accurate! 2320364 151 O Neubert ventral Fro. based parcellation MNI Structural Atlas(show/Hide) volume 4.0%Parietal Lobe(Show /Hide a OxFord Thalamic Co,.wty Probability Atlas BOxford-lmanova Stri... Atlas 3 sub-regions B Oxford-lmanove surl... Atlas 7 sub-regions B Oxford-Imanova Striatal Structural Atlas Osallet Dorsal Frontal. y-based parcellation B Subthalamic Nucleus Atlas 后面对于分割不满意,还可以 Boundary corrected segmentation output,将边界重新进行调整。 Vertex Analysis 照样是跟着教程来,换了一个文件夹,在fs1/ seg struc/ first/ shapeAnalysis下面运行的。 1.百先;使用 concat bvars把顶点信息文件 bvars整合成一个,注意此时整合的顺序和后序 design matrix的要保持一致 2.接着,打开Gm,按照教程上的进行配置 3. Create a design matrix(Don 't worry if you don't fully understand this part, we will cover this in more detail later in the course). The subject order should match the order in which the, bvars were combined in the concat bvars call. The design matrix is most easily created using FSL's GIm tool (a single column file). To do this, start the Glm GUI (Glm gui on mac). First, choose the Higher-level/non-timeseries design option from the top pull down menu in the small window. Next, set the inputs option to be 8(the number of subjects we have in this example) 4. In the bigger window(of the Glm GUI)set the values of the Ev (the numbers in the second column to be -1 for the first five entries (our five controls) and +1 for the next three entries(our three patients ) This will allow us probe the difference between groups. Leave the group column as all ones. Once done this, go to the Contrasts and F-tests tab. Rename the t- contrast(C1)to group difference, but leave the value set for EV1 as 1. We also need to add an F-test. Change the number in the F-tests box to 1, and then highlight the button on the right hand side(under F1)to select an F-test that operates on the single t-contrast This F-test will be the main contrast of interest for our vertex analysis as it allows us to test for differences in either direction 5. When this is all set up correctly, save everything using the Save button in the smaller GIm window, Choose the current directory and use the name design con1 dis2(as we will assume this is the name used below, although for your own studies you can use any name of your choice). Now exit the Glm GUI 6.然后,使用 randomise进行统计检验 7.最后,用 fsleyes可视化。全部命令如下: concat bvars all bvars *L Hipp*bvars 我的理解是,该步骤把不同 subject的 bvars整合出来的文件,使用-- useRecon MNT参数,重构№N工空间。 Reconstructs the me shes in MNI space( native space of the model) first _utils --usebvars --vertexAnalysis -i all bvars -o diff con1 dis2_L Hipp mni -d design con1 dis2. mat --useReconMNI #在重构好的空间里,进行统计检验 randomise -i diff con1 dis2 L Hipp mninii. gz m diff con1 dis2 L Hipp mni mask.nii. gz -o con1 dis2_ L_Hipp rand -d design con1 dis2.mat t design con1 dis2. con -f design con1 dis2. fts fonly -D-F 3 #可视化有差异的地方(都在MNI152空间) fsleyes -stdlmm con1 dis2 L Hipp rand cluster corrp fstat1 -cm red-yellow -dr 0.95 1 最后可视化,橘黄色部分应该就是存在差异的地方 Ortho view 1 x 3D View 2 MNI152 T1 1mm Opacity iorns 30/4D volume Contrast B回⊙回m目甲+Q zoom 10 回+Q0 Overlay list 网 Location Coordinates: MNI152 Voxel location a docent_ dis2__Hipp,rand_cluster.corrp.fstatt con1_disa_ L_Hipp_ra 3379261 1: Overlay list 国 Location ●MN52T1mm 2175005 108 MNI152 T1 1mm 24108565028 Coordinates: MNi152 3-157394 con1 dis2 L Hipp rand cluster corrp fstatt 339261 124 Volume ③MN152T11mm -1750005 1574394 当然, User Guide里面也有相似的内容,也跑了一下,比较坑的是, User Guide内容蛟老,里面有张图是用FsLⅵew可视化的,现在该工具已经弃用,另一张图按照示例代码(见下方),对应着改了下文件,也没有做出来有不同颜色和箭头( User Guide箭头要另做)的样子。做出来的图如下 User Guide链接;htps;/「sl.mrib.ox.ac.uk/s「 slwiki/FIRST/ Userguide #此处的不同之处在于,是在原来图像的空间进行重构 #--useReconNative Reconstructs the meshes in the native space of the image. For vertex-wise stats need to also use --userigidAlign #--useRigidAlign Uses a 6 Degrees of Freedom transformation to remove pose from the meshes (see --useScale if you wish to remove size as well). All

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