Nonrigid registration with differential bias correction using normalised mutual information

Intensity inhomogeneity is an important phenomenon affecting magnetic resonance imaging, which can greatly affect computational image analysis. Bias correction algorithms are commonly used, but are imperfect, leaving residual inhomogeneity that will usually differ in serial images of the same subject. This differential bias can have a detrimental effect on further processing, such as registration and quantification of small longitudinal changes. We embed a differential bias field model within a nonrigid registration framework. The spatial transformation and DBC parameters are optimised concurrently using the normalised mutual information as a metric. We show significant reductions in registration error with the proposed framework.