OPTIMIZED HOMOMORPHIC UNSHARP MASKING FOR MR GRAYSCALE INHOMOGENEITY CORRECTION

Grayscale inhomogeneities in magnetic resonance (MR) images confound q uantitative analysis of these images. Homomorphic unsharp masking and its variations have been commonly used as a post-processing method to remove inhomogeneities in MR images. However, little data is available in the literature assessing the relative effectiveness of these algor ithms to remove inhomogeneities, or describing how these algorithms ca n affect image data. In this study, we address these questions quantit atively using simulated images with artificially constructed and empir ically measured bias fields. Our results show that mean-based filterin g is consistently more effective than median-based algorithms for remo ving inhomogeneities in MR images, and that artifacts are frequently i ntroduced into images at the most commonly used window sizes. Our resu lts demonstrate dramatic improvement in the effectiveness of the algor ithms with significantly larger windows than are commonly used.