CT and MRI image fusion via multimodal feature interaction network

Computed tomography (CT) and magnetic resonance imaging (MRI) image fusion is a popular technique for integrating information from two different modalities of medical images. This technique can improve image quality and diagnostic efficacy. To effectively extract and balance complementary information in the source images, we propose an end-to-end multimodal feature interaction network (MFINet) to fuse CT and MRI images. The MIFNet consists of a shallow feature extractor, a feature interaction (FI), and an image reconstruction. In the FI, we design a deep feature extraction module, which consists of a series of gated feature enhancement units (GFEUs) and convolutional layers. To extract key features from images, we introduce a gated normalization block in the GFEU, which can achieve feature selection. Comprehensive experiments demonstrate that the proposed end-to-end fusion network outperforms existing state-of-the-art methods in both qualitative and quantitative assessments.