Fast algorithm for calculation of inhomogeneity gradient in magnetic resonance imaging data

• Published in: Journal of magnetic resonance imaging 2010-11, Vol.32 (5), p.1197-1208
• Main Authors: Hui, Cheukkai, Zhou, Yu Xiang, Narayana, Ponnada
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; bias field; Brain - anatomy & histology; Computer Simulation; Humans; Image Processing, Computer-Assisted; intensity gradient; intensity inhomogeneity; Magnetic Resonance Imaging - methods; MRI; Rats; surface-fitting
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.22344

Purpose To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. Materials and Methods The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Using a statistically stable method, the algorithm first calculates the partial derivative of the inhomogeneity gradient across the data. The algorithm then solves for the gradient field and fits it to a parametric surface. It was tested on both simulated and real human and animal MRI data. Results The algorithm is shown to restore the homogeneity in all images that were tested. On real human brain images the algorithm demonstrated superior or comparable performance relative to some of the commonly used intensity inhomogeneity correction methods such as SPM, BrainSuite, and N3. Conclusion The proposed algorithm provides an alternative method for correcting the intensity inhomogeneity in MR images. It is shown to be fast and its performance is superior or comparable to algorithms described in the published literature. Due to its generality, this algorithm is applicable to MR images of both humans and animals. J. Magn. Reson. Imaging 2010;32:1197–1208. © 2010 Wiley‐Liss, Inc.