ITERATIVE RECONSTRUCTION METHODS FOR NON-CARTESIAN MRI

For magnetic resonance imaging (MRI) with Cartesian k-spac e sampling, a simple inverse FFT usually suffices for image reconstruction. More sophisticated image reconstruction me thods are needed for non-Cartesian k-space acquisitions. Reg ularized least-squares methods for image reconstruction i nvolve minimizing a cost function consisting of a least-squa res data fit term plus a regularizing roughness penalty that controls noise in the image estimate. Iterative algorithms are usually used to minimize such cost functions. This paper summarizes the formulation of iterative methods for image reco nstruction from non-Cartesian k-space samples, and describ some of the benefits of iterative methods. The primary disadvantage of iterative methods is the increased computatio n time, and methods for accelerating convergence are also dis cussed.

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