Abstract: In medical imaging,MRI images provide detailed texture information and better resolution,while PET/SPECT images retain molecular activity information and color function information,so fusing them is an important task.Most of the existing methods have some problems in the fusion process,such as color distortion,blur and noise.Therefore,a new subspace attention-siamese auto-encoding network(SSA-SAEN) is proposed to fully fuse meaningful information from MRI and PET/SPECT images.SSA-SAEN is proposed in image fusion network,and the subspace feature mutual learning concept is introduced.By using subspace attention module,MRI and PET/SPECT images can learn each other’s features,while reducing information redundancy and ensuring efficient and complete feature extraction.In addition,the conditional probability model is used to complement and fuse the extracted features,and the weighted fidelity gradient loss term is added into the training network to achieve the goal of network optimization.A large number of qualitative and quantitative experiments on public datasets show that the proposed me-thod can obtain a clear fused image,which demonstrates the superiority and effectiveness of the proposed method compared with other advanced methods.

Key words: Subspace attention, Mutual learning, Neural network, MRI and PET/SPECT image fusion