intensity inhomogeneities throughout the human body. The imaging of the whole body can be performed with multiple coils. The images from individual coils suffer from smooth intensity nonuniformities that stem from the inhomogeneities of the radio-frequency (RF) field within the subject. The overall field also suffers from sharp changes in the inhomogeneities at the junctions between regions corresponding to adjacent coils. This is because the MRI signal amplitude across coils is not standardized and inevitably varies between the coils used due to multiple factors. There have been attempts to calibrate for smooth coil nonuniformities in MRI with physical phantoms and parametrized acquisitions [8, 9]. In particular, in whole-body MRI, there is a method for station-to-station adapted \(B_{1}^{+}\) shimming [10]. However, these methods require additional physical acquisitions that are time-consuming and are valid for only particular MRI sequences and anatomies. Several general postacquisition restoration methods have also been proposed. Postacquisition methods for intensity uniformity restoration benefit from regularity properties of the anatomy and of the physical radiofrequency (RF) field as well as from general prior knowledge of the imaging process. They are applicable to a range of MRI contrasts and anatomies. In general, they assume that the nonuniformity is smooth and that the underlying anatomic image is the union of piecewise constant intensity regions corresponding to different uniform tissues with uniform intensity transition magnitudes between them. Some postacquisition methods encode this directly using the total variation in space [11, 12]. Piecewise anatomic constancy has also been encoded with local fuzzy C-means intensity clustering [13]. This method is also combined with level sets for spatial segmentation [14, 15]. The entropy for the sharpness of the histogram has also been used [16]. A more general, non-parametric approach is the Bayesian coring framework. The image histogram has been analyzed with coring to remove noise [17, 18]. Coring has been adapted for low-frequency noise to offer a smooth nonuniformity [19]. Current postacquisition methodologies in medical image analysis cannot accommodate sharp changes between intensity nonuniformities of different regions. Moreover, the direct histogram-based corrections can lead to an unstable dynamic range and hence unstable correction as well [19]. A clinical imaging protocol consists of multiple sequences providing images that suffer from different nonuniformities. Postacquisition methods have been developed for their joint restoration. A data dependent method fuses MRI with PET data [20]. A variational method preserves the differential structure of two images [21] and enforces a smooth nonuniformity. A statistical method minimizes the entropy of the joint histogram of two images [22]. Another method restores multiple co-registered images by minimizing the sum of the entropies of voxelwise stack vectors throughout an image [23]. In general, there has been significantly more work on homogenization of the intensities of an image rather than on standardization of the intensities of different parts of an image or standardization of intensities between several images. There have been efforts to account for the lack of intensity standardization in MRI by using physical arguments to compute physical MR imaging parameters of the tissues. These methods reconstruct intermediate homogeneous physical images [24, 25]. Several postacquisition restoration methods have also been proposed. Histograms of pairs of images have been matched with piecewise linear transformations for brain images [26]. The difference between intensity ranges of different slices in a scan has been modeled with a multiplicative factor [27]. This multiplicative factor can vary for different slices. Histograms of images that can even be multicontrast have been normalized with the Kullbach-Leibler divergence [28].