Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion

Abstract

Magnetic resonance imaging (MRI) is a major imaging technique for studying neuroanatomy. By applying different pulse sequences and parameters, different modalities can be generated regarding the same anatomical structure, which can provide complementary information for diagnosis. However, limited by the scanning time and related cost, multiple different modalities are often not available for the same patient in clinic. Recently, many methods have been proposed for cross-modality MRI synthesis, but most of them only consider pixel-level differences between the synthetic and ground-truth images, ignoring the edge information, which is critical to provide clinical information. In this paper, we propose a novel edge-preserving MRI image synthesis method with iterative multi-scale feature fusion based generative adversarial network (EP_IMF-GAN). Particularly, the generator consists of a shared encoder and two specific decoders to carry out different tasks: 1) a primary task aiming to generate the target modality and 2) an auxiliary task aiming to generate the corresponding edge image of target modality. We assume that infusing the auxiliary edge image generation task can help preserve edge information and learn better latent representation features through the shared encoder. Meanwhile, an iterative multi-scale fusion module is embedded in the primary decoder to fuse supplementary information of feature maps at different scales, thereby further improving quality of the synthesized target modality. Experiments on the BRATS dataset indicate that our proposed method is superior to the state-of-the-art image synthesis approaches in both qualitative and quantitative measures. Ablation study further validates the effectiveness of the proposed components.