When reconstructing with traditional Fourier-based techniques, image distortions arising from nonlinear B1 and B0 inhomogeneity can plague radiofrequency-based (RF) imaging methods that rely on B1 gradients for spatial encoding. In this work, we propose a new framework for reconstructing multi-dimensional RF gradient-based images leveraging an iterative approach to solve a regularized inverse problem. The proposed methodology employs a full Bloch simulation to reconstruct an undistorted image after determination of the forward operator and measured receive coil sensitivities.
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