tract: Multimodal deformable image registration is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in deformable image registration. Most deep learning approaches use the so-called mono-stream "high-to-low, low-to-high" network structure, and can achieve satisfactory overall registration results. However, accurate alignments for some severely deformed local regions, which are crucial for pinpointing surgical targets, are often overlooked, especially for multimodal inputs with vast intensity differences. Consequently, these approaches are not sensitive to some hard-to-align regions, e.g., intra-patient registration of deformed liver lobes. In this paper, we propose a novel unsupervised registration network, namely Full-Resolution Residual Registration Network (F3RNet), for multimodal registration of severely deformed organs. The proposed method combines two parallel processing streams in a residual learning fashion. One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition. We also factorize the 3D convolution to reduce the training parameters and enhance network efficiency. We validate the proposed method on 50 sets of clinically acquired intra-patient abdominal CT-MRI data. Experiments on both CT-to-MRI and MRI-to-CT registration demonstrate promising results compared to state-of-the-art approaches.