A technical challenge in the registration of dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging in the liver is intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, a manifold-based registration framework for liver DCE-MR time series is proposed. We assume that liver DCE-MR time series are located on a low-dimensional manifold and determine intrinsic similarities between frames. Based on the obtained manifold, the large deformation of two dissimilar images can be decomposed into a series of small deformations between adjacent images on the manifold through gradual deformation of each frame to the template image along the geodesic path. Furthermore, manifold construction is important in automating the selection of the template image, which is an approximation of the geodesic mean. Robust principal component analysis is performed to separate motion components from intensity changes induced by contrast agents; the components caused by motion are used to guide registration in eliminating the effect of contrast enhancement. Visual inspection and quantitative assessment are further performed on clinical dataset registration. Experiments show that the proposed method effectively reduces movements while preserving the topology of contrastenhancing structures and provides improved registration performance. Chronic liver diseases, including cirrhosis and fibrosis, are associated with high morbidity and mortality worldwide. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to noninvasively quantify regional and global changes in hepatic perfusion associated with liver cirrhosis and fibrosis. Changes in longitudinal intensities of the voxels in DCE-MR time-series images reflect the dynamic change in the concentration of contrast agents in tissues, which can be employed to extract quantitative parameters to quantify tumor perfusion, vascular volume, vessel permeability, and angiogenesis. A pharmacokinetic (PK) model is a widely used classical model that fits the changes in contrast agent concentration over time at each voxel [1] [2] [3] [4] [5] . However, the accuracy of PK analysis is susceptible to patient motion (e.g., inconsistent breath holding and stomach and bowel peristalsis), and such disturbance could result in voxel mis-correspondence in successive images in DCE-MRI time series. Therefore, registration of DCE-MR time-series images is a crucial preprocessing step for PK analysis and has been studied intensively in previous studies. The following factors should be considered in the registration for DCE-MR time series. Intensity variations caused by contrast agent can corrupt the correspondence between the voxels of pre-and post-contrast images. The desired registration method should be capable of coping with intensity inconsistencies caused by contrast agent and patient motion, respectively. As a series of images need to be aligned, the registration of DCE-MR time-series images can be considered a group-wise registration problem. New techniques of group-wise registration, such as template image selection and registration route optimization, which are ignored in most existing methods, can benefit the registration of DCE-MR time series. Existing registration algorithms for DCE-MR time series focus on intensity variances. Because contrast-enhanced regions always expand or shrink after registration using traditional methods, several specific adjustments to the cost function or optimization method have been proposed. One example is locally rigid transformation 6 , which can penalize deviations of the Jacobian determinant of deformation 7 to preserve the local
Recently, Feng et al. proposed a manifoldbased registration framework for liver DCE-MRI, that also relies on a decomposition of motion-and CA-related image changes REF .
Liver DCE-MRI Registration in Manifold Space Based on Robust Principal Component Analysis
Scientific Reports
Scientific Reports
Computer Science Medicine