Gradient Field Deviation (GFD) Correction Using a Hybrid-Norm Approach With Wavelet Sub-Band Dependent Regularization: Implementation for Radial MRI at 9.4 T

In magnetic resonance imaging (MRI), system imperfections and eddy currents can cause gradient field deviation (GFD), leading to various image distortions, such as increased noise, ghosting artifacts, and geometric deformation. These distortions can degrade the clinical value of MR images. Generally, non-Cartesian image sequences, such as radial sampling, produce larger gradient deviations than Cartesian sampling, as a result of stronger eddy current-induced gradient delays and phase errors. In this paper, we developed a GFD encoding method to reduce image noise and artifacts for radial MRI. In the proposed method, a hybrid norm (combination of L2 and L1 norms) optimization problem was formed, which incorporated a wavelet sub-band adaptive regularization mechanism. The new approach seeks a regularized solution not only offering corrected images with reduced artifacts and geometric deformation, but also good preservation of anatomical structural details. The new method was evaluated with simulation and experiment at 9.4 T MRI. The results demonstrated that the proposed method can provide over 50% noise reduction and 15% artifact reduction compared with the traditional regridding method, suggesting substantially reduced GFD-induced distortions and improved image quality.