Evaluation of Eddy-current distortion and EPI distortion corrections in MR diffusion imaging using log-demons DIR method

Purpose: To investigate the feasibility of the Log-Demons deformable image registration (DIR) method to correct eddy current and Echo Planar Imaging (EPI) distortions while preserving diffusion tensor information.

Methods: A phantom MR scan was conducted using a diffusion phantom scan (Diffusion Phantom Model 128, High Precision Devices, Inc) on a clinical 3T scanner. The scan includes a standard T1-weighted scan and a 20‐direction diffusion tensor imaging (DTI) scan, which consists of one data set with b=0s/mm2 and twenty diffusion-weighted data sets with b=1,000s/mm2. A Log-Demons DIR algorithm was applied to the DTI images for eddy current and EPI distortion correction based on the b=0s/mm2 and T1 weighted data sets and compared the eddy current and EPI distortion corrections along the phase encoding direction by affine and demons DIR algorithms. The Log-Demons framework is optimized based on both similarity and regularization. The registered images were analyzed using Cross-correlation (CC) and mutual information (MI) to assess the performances of distortion corrections by the DIR methods. Quantitative deviations from the original data after correction were also evaluated using the mean, and root mean square error (RMSE) for thirteen regions of interest in the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) maps.

The Log-Demons DIR algorithm was then applied to the MASSIVE dataset, which provides diffusion-weighted volumes divided into four sets with both positive (+) and negative(-) diffusion gradient directions and both AP and PA phase encoding directions. The registered images were analyzed using the mutual information (MI) and the absolute mean difference of two images with opposing gradient directions to assess the performances of distortion corrections by the DIR methods. Images with opposing gradient directions were compared when comparing eddy current distortions and images with opposing phase encoding directions were compared for EPI distortions.

Results: In the phantom study, the MI and CC were improved by 2.15%,0.89%, and 39.39% compared to no correction, and affine, and demons algorithm respectively when correction for eddy current distortions. MI and CC were improved by 8.89%, 9.33%, and 9.20% compared to no correction, and affine, and demons algorithm respectively when correction for EPI distortions. Analysis of the tensor metrics using percent difference and the RMS of the ADC and FA found that the Log-Demons algorithm outperforms the other algorithms in terms of preserving diffusion information.

In the MASSIVE study, the Log-demons DIR method outperformed the demons algorithm in terms of MI but underperformed compared to the affine registration for both eddy current and EPI distortions corrections. The absolute mean difference was decreased by 2.94%, 0.44%, and 1.53% compared to no correction, and affine, and demons algorithm respectively when correcting for eddy current distortions, and decreased by 0.39%, 8.03%, and 13.19% compared to no correction, and affine, and demons algorithm respectively when correcting for EPI distortions.

Conclusion: This work indicates that the Log-Demons DIR algorithm is feasible to reduce eddy current and EPI distortions while preserving quantitative diffusion information. Although demonstrated with a DTI phantom study and brain study, this method could be extended for areas in which diffusion-weighted imaging is beneficial.