In Dynamic Contrast-Enhanced MRI (DCE-MRI) of the liver, a series of images is acquired over a period of 20 minutes. Due to the patient’s breathing, the liver is subject to a substantial displacement between acquisitions. Furthermore, due to its location in the abdomen, the liver also undergoes marked deformation. The large deformations combined with variation in image contrast make accurate liver registration challenging. We present a registration framework that incorporates a liver segmentation to improve the registration accuracy. The segmented liver serves as region-of-interest to our in-house developed registration method called ALOST (autocorrelation of local image structure). ALOST is a continuous optimization method that uses local phase features to overcome space-variant intensity distortions. The proposed framework can confine the solution field to the liver and allow for ALOST to obtain a more accurate solution. For the segmentation part, we use a level-set method to delineate the liver in a so-called contrast enhancement map. This map is obtained by computing the difference between the last and registered first volume from the DCE series. Subsequently, we slightly dilate the segmentation, and apply it as the mask to the other DCE-MRI volumes during registration. It is shown that the registration result becomes more accurate compared with the original ALOST approach.
Image registration of medical images in the presence of large intra-image signal fluctuations is a challenging task. Our paper addresses this problem by introducing a new concept based on the structure tensor of the local phase. The local phase is calculated from the monogenic signal representation of the images. The local phase image is hardly affected by unwanted signal fluctuations due to a space-variant background and a space-variant contrast. The boundary structure tensor combines the responses of edges and corners/junctions in one tensor, which has several advantages, compared to other structure tensors. We reorient the structure tensor during the registration by means of the finite-strain technique. The structure tensor is only calculated once during a preprocessing step. The results demonstrate that the proposed method effectively deals with large signal fluctuations. It performs significantly better than competing techniques.