Multimodality liver registration of Open-MR and CT scans

Purpose Multimodality registration of liver CT and MRI scans is challenging due to large initial misalignment, non-uniform MR signal intensity in the liver parenchyma, incomplete liver shapes in Open-MR scans and non-rigid deformations of the organ. An automated method was developed to register liver CT and open-MRI scans. Methods A hybrid registration algorithm was developed which incorporates both rigid and non-rigid methods. First, large misalignment of input CT and Open-MR images was compensated by intensity-based registration. Maximum intensity projections (MIPs) of CT and MR data were registered in 2D, and the corresponding rigid transform parameters were used to align 3D images in axial, coronal and sagittal planes. Use of MIP projections compensates for intensity inhomogeneities inherent in the Open-MR data. A bounding box of MIP images defines an ROI which removes outliers and copes with incomplete MR data. Next, principal components analysis (PCA) was used to align MR and CT data datasets. The corresponding translation and rotation parameters were then used to increase the global registration accuracy. A modified TPS-RPM point-based non-rigid algorithm was used to accommodate local liver deformations. Surface points on the liver and branching points of the portal veins were input as landmarks to TPS-RPM method. Incorporating vascular branching points improves registration since tumors are usually found near vessels, so greater weight was given to branching points compared with surface points. Results The automated registration algorithm was compared with both rigid and non-rigid methods. Quantitative evaluation was performed using modified Hausdorff distance and overlap measure. The mean modified Hausdorff distances of liver and tumor were decreased from 23.53 and 40.03 mm to 9.38 and 8.88 mm, respectively. The mean overlap measures of liver and tumor were increased from 39 and 0 % to 78 and 27 %, respectively. Statistical analysis of the outcomes resulted in a p value less than 5 %. Conclusion MIP-PCA-based rigid multimodality CT–MRI registration of liver scans compensates for large misalignment of input images even when the data are incomplete. A modified TPS-RPM algorithm, in which vascular points are emphasized over surface points, successfully handled local deformations.