Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging

Pre-excitation gradients for eddy current nulled convex optimized diffusion encoding (Pre-ENCODE)

PURPOSE

To evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI (DWI).

METHODS

DWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE  min $$ {}_{\mathrm{min}} $$ ) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers (N = 6 $$ N=6 $$ ).

RESULTS

Pre-ENCODE provided a shorter TE  min $$ {}_{\mathrm{min}} $$ than MONO (71.0± $$ \pm $$ 17.7ms vs. 77.6± $$ \pm $$ 22.9ms) and ENCODE (71.0± $$ \pm $$ 17.7ms vs. 86.2± $$ \pm $$ 14.2ms) in 100% $$ \% $$ of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm  2 $$ {}^2 $$ and in-plane spatial resolutions ranging from 1.0 to 3.0mm  2 $$ {}^2 $$ . Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1% $$ \% $$ vs. 22.7% $$ \% $$ ,p = 6 - 5 $$ p={6}^{-5} $$ ) and comparable to ENCODE (10.1% $$ \% $$ vs. 10.4% $$ \% $$ ,p = 0 . 12 $$ p=0.12 $$ ). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45]× 1 0 - 3 $$ \times 1{0}^{-3} $$ mm  2 $$ {}^2 $$ /s vs. 0.38 [0.28,1.45]× 1 0 - 3 $$ \times 1{0}^{-3} $$ mm  2 $$ {}^2 $$ /s,p = 0 . 25 $$ p=0.25 $$ ) and increased in edge brain pixels (0.80 [0.17,1.49]× 1 0 - 3 $$ \times 1{0}^{-3} $$ mm  2 $$ {}^2 $$ /s vs. 0.70 [0.18,1.48]× 1 0 - 3 $$ \times 1{0}^{-3} $$ mm  2 $$ {}^2 $$ /s,p = 0 . 02 $$ p=0.02 $$ ) for MONO compared to Pre-ENCODE.

CONCLUSION

Pre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE  min $$ {}_{\mathrm{min}} $$ than MONO and ENCODE.

On the down-sampling of diffusion MRI data along the angular dimension

BACKGROUND

It has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties.

MATERIALS AND METHODS

Here we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices.

RESULTS

Our experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index.

CONCLUSIONS

These results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.