An Efficient MR Inhomogeneity Corrector Using Regularized Entropy Minimization

MR images are usually degenerated by artifact of intensity inhomogeneity, or bias field, undesirable for perception and diagnosis. In this work, we present an optimized 3-dimensional retrospective nonparametric inhomogeneity correction method by minimizing a regularized-entropy criterion. The inhomogeneity estimator is numerically particularly efficient, scalable and parallelizable compared to exisiting entropy-based approaches. Its effectiveness and robustness have also been validated by vast clinical evaluations on 1.5T and 3T scans of brain and breast applications.

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