You will also need to acquire a reference image with no diffusion information to calculate the ADC value (divide diffusion scan by reference scan). You can vary the amount of diffusion weighting (level of sensitivity to diffusion). The amount of diffusion weighting is measured by the b-value. If the b-value equals 0, there is no diffusion weighting (reference scan). If the diffusion value is very high, you can get greater resolution, but also more noise. The standard b-value for adults is 1000. In children, you typically use 600. If looking outside the brain, typically around 500.
converted to NIfTI format and includes the b-values and gradient idrections: .bval and .bvec
mean B0 image is not sensitive to diffusion direction.
in diffusion image, the brightness varies dramatically in the white matter depending on the alignment of the fibers. More diffusion yields a darker pixel because you lose signal as the water molecule can go anywhere. Higher ADC values mean it can go very far without interruption, and it takes the signal with it. This is not necessarily related to anisotropy.
Eddy current correction of DTI data is analogous to motion correction of fMRI data.
Processing Data with FSL’s FDT Diffusion
The reference volume is typically 0 (this is the volume with a b-value of 0, dcm2nii should automatically ensure that the initial volume is the volume with zero b-value, but you should ensure this is correct with your data which you can do by viewing the volumes with MRIcron).
The basic processing pipeline has the following elements:
Convert data from scanner to scalar image
Run distortion correction
EPI distortion correction with fieldmap/TOPUP
Eddy current correction
Brain extraction
Tensor fitting
Produce scalars (FA, MD, AD, RD, RGB)
Advanced processing:
Normalization
Scalar normalization, or
Tensor normalization
Fiber tracking
Deterministic, or
Probabilistic
Whole brain analysis
Voxel-based analysis, or
Track-based analysis
步骤:FA -> Tensors -> Fiber tracking -> white matter delineation
http://www.cabiatl.com/Resources/Course/tutorial/html/dti.html
http://brainimaging.waisman.wisc.edu/~tromp/DTI_101.pdf
http://www.diffusion-imaging.com/2015/10/dti-tutorial-1-from-scanner-to-tensor.html
https://mrtrix.readthedocs.io/en/latest/quantitative_structural_connectivity/ismrm_hcp_tutorial.html
http://dbic.dartmouth.edu/wiki/index.php/Diffusion_Tensor_Imaging_Analysis