Abstract
A method for the reconstruction of magnetic resonance images that allows for a substantial reduction of the quantity of measured data and, therefore, of the acquisition time is described. The truncation artifact is also reduced, improving the image quality. The method is based on techniques for getting superresolution in spectral analysis such as autoregressive modeling and Prony's method. Moreover, some new ideas about the autoregressive order selection are introduced. The method is compared to the standard one for reconstructing simulated, phantom, and medical magnetic resonance images. The numerical stability and the computational cost issues of the resulting algorithm are also addressed.
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