Radiologist and clinical researchers who are interested in liver perfusion quantification and post-processing.We aim at providing a practical dynamic contrast enhanced MRI (DCE-MRI) liver registration tool, with high speed for clinical research. Quantitative analysis on liver function has become increasingly important for DCE-MRI and the diagnosis of lesions. However, the quantification was often misconducted due to motion-induced mismatch of images among different dynamic phases. Image registration approaches make it possible to analyze the haemodynamic change in liver on a voxel or region of interest (ROI) level. Since multiple sources of motion contributed to the mismatch, a non-rigid registration is required. Previous methods usually take a few minutes to register one dynamic scan images, which is unacceptable for clinical practice. This study proposes a rapid 3D non-rigid registration tool for DCE liver registration by optimizing the scheme of image matching. To evaluate the registration performance, computation time and ROI analysis have been shown. Clinical routine DCE-MRI was conducted on a 61 year-old male subject with lung cancer suffering diarrhea, in the 2nd affiliated Hospital of Soochow University. The imaging was performed on a 3 T Ingenia system (Philips Healthcare, the Netherlands), with acquisition of 64 slices per scan with 12 dynamic scans (parameters: TR = 3.51ms, flip angle = 10°, FOV = 400*400 mm2, matrix size: 480*480, slice thickness = 3 mm). Contrast agent was given after acquiring three scans as reference phase. Image registration was applied on the image series to align all images to the 1st time point image. The registration was based on freeform deformation [1] and mutual information to enable application on images with different contrasts [2, 3], e.g. DCE images. We reduced the number of sampled points for fast calculation of transformation, so that the computation time was greatly reduced. Registration was performed using an HP Z620 Workstation. For evaluation of registration performance, three-dimensional whole liver boundary and the main portion of portal vein were delineated manually on the 1st time point image and the mean intensities of the two ROIs on each time point were calculated.No abnormality of any abdomen organ was detected on the subject by radiologist. Selected time point images from preparation phase and 3 perfusion phases were shown in Fig.1 to evaluate the registration performance. The dynamic images were well registered to the pre-contrast image, with respect to both the whole liver boundary (ROI in cyan) and the portal vein (ROI in pink). Red arrows in Fig.1 highlighted the severe mis-match of original scans. Fig.2 demonstrated the intensity plot of the ROIs. The mean intensity curve after registration (Fig.2 - right) matched the previous report from literature [4], while quantification from original images (Fig.2 - left) was contaminated by mis-match error. Computation performance of registration was shown in Table 1. The registration only took around 5 seconds for aligning one scan image, with memory used of 472 megabytes. The total time for registration was within 1 minute, much faster than the traditional non-rigid 3D registration method [5]. This acceleration was achieved by the optimized portion of sampled voxels for calculation of the transformation.The optimized 3D liver registration tool rapidly aligns DCE liver images with local contrast changes. The registration improves the ROI-based quantification of liver and portal vein. The good alignment of liver and other tissue organs will be helpful for voxel-based analysis of lesion and k-trans analysis. Because of the rapid computation with satisfied registration performance, this tool is promising to be applied in DCE image analysis.