Automated model-based bias field correction of MR images of the brain

We propose a model-based method for fully automated bias field correction o f MR brain images. The MR signal is modeled as a realization of a random pr ocess with a parametric probability distribution that is corrupted by a smo oth polynomial inhomogeneity or bias field. The method we propose applies a n iterative expectation-maximization (EM) strategy that interleaves pixel c lassification with estimation of class distribution and bias field paramete rs, improving the likelihood of the model parameters at each iteration, The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atl as about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction a lgorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed al gorithm to other bias correction algorithms.