Image-based estimation method for field inhomogeneity in brain echo-planar images with geometric distortion using k-space textures

Echo-planar imaging (EPI) can suffer from geometrical distortion due to magnetic field inhomogeneity. To correct the geometric distortions in EPI, a magnetic field map is used. Our purpose was to develop a novel image-based method for estimating the field inhomogeneity map from the distorted EPI image and T1-weighted image of the brain using k-space textures. Based on magnetic resonance imaging physics, our method synthesizes the distorted image to match the measured EPI image through the generating process of EPI image by updating the estimated field inhomogeneity map. The estimation process was performed to minimize the cost function, which was defined by the synthesized EPI image and the measured EPI image with geometric distortion, using an iterative conjugate gradient algorithm. The proposed method was applied to simulation and human data. To evaluate the performance of the proposed method quantitatively, we used the normalized root mean square error (NRMSE) between the ground truth and the results estimated by our proposed method. In simulation data, the values of the NRMSE between the ground truth and the estimated field inhomogeneity map were <0.08. In both simulation and human data, the estimated EPI images were very similar to input EPI images, and the NRMSE values between them were <0.09. The results of the simulated and human data demonstrated that our method produced a reasonable estimation of the field inhomogeneity map. The estimated map could be used for distortion correction in EPI images.