Deformable MRI-CT liver image registration using convolutional neural network with modality independent neighborhood descriptors

In radiotherapy, liver CT images are routinely acquired for treatment planning since CT images can provide important electron density information for dose calculation in treatment planning system. Besides the CT images, MRI is often acquired for diagnosis since it has superior soft tissue contrast. Therefore, for accurate contouring of the target, liver CT and MRI image registration plays an important role. It is very challenging to register the CT and MRI liver images due to the distinct image intensities and liver motion. In this study, a deep learning-based network was proposed to directly predict the DVF for MRI-CT liver image registration. To overcome the challenge of multimodal registration, we propose to incorporate a modality independent descriptor into the deep learning network to explore the correlations between the MRI and CT images. Our results show that the proposed network could accurately align MRI to CT liver images. The average Dice increased from 0.89±0.04 before registration to 0.93±0.02 after registration.