Bimodal Nonrigid Registration of Brain MRI Data With Deconvolution of Joint Statistics

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired data sets often need to be coregistered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this paper, a novel and effective nonrigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multicontrast data sets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.