  Multi-modal magnetic resonance (MR) imaging provides great potential for
diagnosing and analyzing brain gliomas. In clinical scenarios, common MR
sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single
scanning process. However, acquiring contrast enhanced modalities such as T1ce
requires additional time, cost, and injection of contrast agent. As such, it is
clinically meaningful to develop a method to synthesize unavailable modalities
which can also be used as additional inputs to downstream tasks (e.g., brain
tumor segmentation) for performance enhancing. In this work, we propose an
end-to-end framework named Modality-Level Attention Fusion Network (MAF-Net),
wherein we innovatively conduct patchwise contrastive learning for extracting
multi-modal latent features and dynamically assigning attention weights to fuse
different modalities. Through extensive experiments on BraTS2020, our proposed
MAF-Net is found to yield superior T1ce synthesis performance (SSIM of 0.8879
and PSNR of 22.78) and accurate brain tumor segmentation (mean Dice scores of
67.9%, 41.8% and 88.0% on segmenting the tumor core, enhancing tumor and whole
tumor).
