DCAN: A Dual Cascade Attention Network for Fusing Pet and MRI Images

Traditional fusion approaches and most deep learning-based methods usually generate the intermediate decision map, resulting in detail loss of source images or fusion results. In this work, to enhance the detailed features and structured information from source images, we propose a dual cascade attention network (DCAN) to obtain a more informative fusion image for PET and MRI images. In our approach, channel attention is employed to improve the ability of features representation and spatial attention can highlight informative regions in the proposed fusion network. Additionally, channel and spatial attention are sequential arrangement in channel-first. Moreover, to achieve good performance in the procedure of feature extraction and image reconstruction, two-stage training strategy is adopted to train our fusion model. Experimental results demonstrate that the proposed approach achieves remarkable performance for PET and MRI images fusion.