Dear HCP Team, I would like to use HCP diffusion data to do some diffusion tensor fitting and subsequently calculate maps of FA, MD, etc. For this I think it would be handy to use the followings: $subject\T1w\Diffusion\bvals $subject\T1w\Diffusion\bvecs $subject\T1w\Diffusion\data.nii.gz
We can refer to these files as the result of the 'minimal processing pipeline' (Glasser 13 Neuroimage), as this diffusion data is already processed for motion and B0 inhomogeneities among many other artifacts. But since I would like to do some custom-made model estimation (aka. outside of FSL/dtifit/bedpostx), I need to implement a voxelwise bvecs rotation and bval calculation, because of the effect of the diffusion gradient nonlinearities. To do this the source is the $subject\T1w\Diffusion\grad_dev.nii.gz file. A bit of matlab script is provided to calculate the proper bvec and bval per voxel. (this guy: http://www.humanconnectome.org/storage/app/media/documentation/data_release/Q1_Release_Appendix_II.pdf ). Generally: I am right with the above? In the mentioned paper this part is a bit confusing for me (highlighted some parts): "The gradient nonlinearity correction warpfield is then calculated for the diffusion data to remove this spatial distortion (Jovicich et al., 2006), and the mean b0 image is distortion corrected. *Additionally, the effects of gradient nonlinearity on the diffusion encoding magnitudes and directions are calculated (Bammer et al., 2003; Sotiropoulos it et al., 2013–this issue).* The partial derivatives of the spatially-dependent magnetic field are used to calculate a gradient field tensor at each voxel, which maps “nominal” to actual gradient magnitudes and directions (Bammer et al., 2003). *Using the gradient field tensor, the magnitude and direction of the diffusion-sensitizing gradients can be corrected at each brain voxel.* This information is subsequently used for more accurate fiber orientation estimation." In short: the diffusion weighted gradient nonlinearities are calculated, BUT not applied. That is why every time one wants to do model estimation diffusion gradient nonlinearities must be considered in a form to use different bvals and bvecs per voxel. In FSL for dtifit that would be calling the --gradnonlin option. In contrast, distortions from (regular) imaging gradients are corrected, after topup+eddy based correction for B0 inhomogeneities and eddy current distortion correction. This is stated right after the cited paragraph from the same paper: "It is worth noting that the gradient nonlinearity correction is done at a much later stage in the diffusion pipeline than in the fMRIVolume pipeline.* Ideally the gradient nonlinearity distortion correction would be done simultaneously with the B0 inhomogeneity distortion correction, as well as with the eddy current distortion correction and rigid-body motion correction, as all of these interact*." -> but here the "gradient nonlinearity distortion correction" is about the imaging gradients, right? The correction of diffusion gradient nonlinearities are *still* stored in the grad_dev.nii.gz file and must be used during model estimation. Apologies for the long email. Best, Szabolcs _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
