Hi Matt,

Thanks a lot for the clarification.

Best,
Szabolcs

On Fri, Aug 11, 2017 at 2:32 AM, Glasser, Matthew <glass...@wustl.edu>
wrote:

> It looks like you have the correct understanding of this.  One cannot
> easily apply the gradient nonlinearity effects on the diffusion gradients
> to the images, as really you need to apply them to the bvals and bvecs.
>
> Peace,
>
> Matt.
>
> From: <hcp-users-boun...@humanconnectome.org> on behalf of Szabolcs David
> <davidszabolc...@gmail.com>
> Date: Thursday, August 10, 2017 at 7:03 PM
> To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org>
> Subject: [HCP-Users] DTI on HCP data
>
> ​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
>
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