A local-field extrapolation algorithm for improving the spatial resolution in magnetic resonance dynamic imaging

In magnetic resonance imaging (MRI), data are collected as spectrum samples. The acquisition time is proportional to the number of the spectrum lines. Therefore, only few lines of the data space may be required in order to track rapid changes of an object. In the current techniques, the missed lines may be zeroed or replaced by the corresponding lines in a reference image, which is acquired a priori for the same anatomical cross-section. However, this always comes at the expense of the spatial-resolution. In this study, we propose an extrapolation iterative algorithm to provide an improved estimate of the missed lines. Additional spatial and spatial-frequency constraints of the reference image are incorporated to enhance the convergence and obtain a better estimate of the initial conditions of the iterations. Results from simulated data verify the theory and indicate that the algorithm may provide better reconstruction in dynamic imaging studies.