Despite this, some image artefacts were presented, likely being a consequence of gibbs artefacts and MRI noise. In particular, some low intensity voxels were presented in regions where we expect that MK and RK is high. To correct these artefacts, I decide to add a pre-processing step that denoises diffusion-weighted data (to see the coding details of this, see directly on my pull request).
Before fitting DKI on the denoised data, this are the amazing kurtosis maps that I obtained:
|Figure 1 - Real brain parameter maps of the mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) obtain from a HCP-like dataset using the DKI module. These are the maps specific to DKI. The dataset for these reconstructions was kindly supplied by Valabregue Romain, CENIR, ICM, Paris.|
You can also see the standard diffusion measures obtain from my implemented DKI module and compared to the DTI module previously implemented:
|Figure 2. Real brain parameter maps of the diffusion fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) obtain from a HCP-like dataset using the DKI modules (upper panels) and the DTI module (lower panels). Despite DKI involves the estimation of a larger number of parameter, the quality of the diffusion standard measures of the HCP-like dataset from DKI seem be comparable with the standard diffusion measures from DTI. This dataset was kindly supplied by Valabregue Romain, CENIR, ICM, Paris.|