Room: AAPM ePoster Library
Purpose: To investigate the feasibility of the Log-Demons deformable image registration (DIR) method to correct 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. Before EPI distortion correction, an affine method is first used to correct Eddy current distortion of the diffusion-weighted data sets. After, a Log-Demons DIR algorithm was applied to the DTI images for EPI distortion correction based on the T1-weighted data set and compared to EPI distortion corrections by affine and demons DIR algorithms. The Log-Demons framework is optimized based on both similarity and regularization. The registered images were analyzed using mutual information (MI) and Cross-correlation (CC) 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.
Results: MI and CC were improved by 3.80%, 8.96%, and 4.38% compared to no correction, and affine, and demons algorithm respectively. Analysis of the tensor metrics using percent difference and the RMSE of the ADC and FA found that the Log-Demons algorithm outperforms the other algorithms in terms of preserving diffusion information.
Conclusion: This work indicates that the Log-Demons DIR algorithm is feasible to reduce EPI distortion while preserving quantitative diffusion information. Although demonstrated with a DTI phantom study, this method could be extended for areas in which diffusion-weighted imaging is beneficial.
Funding Support, Disclosures, and Conflict of Interest: Funding for this research has been provided by the Duke Cancer Institute