1.终端输入fsl后打开BET
输入图像位置，生成图像位置，设置图像密度阈值f（f值越小，切割大脑轮廓设定越大）, 选择bet执行算法，点击Go运行
2.参数选择（点击Advanced options）
-f <f> fractional intensity threshold (0->1); default=0.5; smaller values give larger brain outline estimates
下图图灰色的是f=0.2的结果,蓝色是f=0.8的结果，如果初始参数设置后发现脑壳剥多了，将f参数改小，留下较大的大脑区域
-g <g> vertical gradient in fractional intensity threshold (-1->1); default=0; positive values give larger brain outline at bottom, smaller at top
如果剥脑壳后下部区域有脖子，额叶顶叶区域不完整，应设为负数，且越小顶部保留越多，底部保留越少
-r <r> head radius (mm not voxels); initial surface sphere is set to half of this
如果被试头部不够标准，可以修改大脑的半径以保留全部大脑区域
-c < x y z> centre-of-gravity (voxels not mm) of initial mesh surface.
定义大脑中心位置，在mricron中打开原图后手动选定大脑区域的中心坐标，如下图Z值过大，应将矢状面的坐标向左移
切割算法选择如下，一般使用自定义算法即可，通过调整参数修正剥脑壳
(自定义) run bet2
-R "robust" brain centre estimation
-S cleanup residual eye and optic nerve voxel
-B reduce image bias, and residual neck voxels
-Z improve the brain extraction if only a few slices are present in the data
-F uses bet2 to determine a brain mask on the basis of the first volume in a 4D data set, and applies this to the whole data set
-A runs both bet2 and betsurf programs in order to get the additional skull and scalp surfaces created by betsurf.
-A2 <T2> This is the same as -A except that a T2 image is also input, to further improve the estimated skull and scalp surfaces.