Suppression of rotational motion artifacts in MRI using a fuzzy data model and POCS with soft constraints

Rotational motion artifacts in magnetic resonance (MR) images can have a significantly adverse effect on the diagnostic value of the image. Post-processing techniques for the suppression of such artifacts often involve re-gridding of the acquired k-space data. This technique creates significant data void regions in the corrected k-space. Projection onto convex sets (POCS) proved to be successful in estimating the missing k-space values. However, the available data are often subjected to noise and interpolation errors which cause the k-space constraints to form an ill defined convex set, inconsistent with the spatial constraints. Inconsistent constraints often lead to divergence of POCS from the desired solution, within a finite number of iterations. Here, the authors propose a fuzzy model for representing the available k-space values to avoid such divergence. They also present a set of fuzzy rules, to impose soft k-space constraints. Simulation results indicate that the proposed algorithm avoids divergence, producing a superior quality final image