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Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonanceimaging (MRI) modalities for diagnosing and analyzing brain tumors, especiallygliomas. In clinical practice, common MRI modalities such as T1, T2, and fluidattenuation inversion recovery are relatively easy to access while T1ce is morechallenging considering the additional cost and potential risk of allergies tothe contrast agent. Therefore, it is of great clinical necessity to develop amethod to synthesize T1ce from other common modalities. Current paired imagetranslation methods typically have the issue of requiring a large amount ofpaired data and do not focus on specific regions of interest, e.g., the tumorregion, in the synthesization process. To address these issues, we propose aDifficulty-perceived common-to-T1ce Semi-Supervised multimodal MRI Synthesisnetwork (DS3-Net), involving both paired and unpaired data together withdual-level knowledge distillation. DS3-Net predicts a difficulty map toprogressively promote the synthesis task. Specifically, a pixelwise constraintand a patchwise contrastive constraint are guided by the predicted difficultymap. Through extensive experiments on the publiclyavailable BraTS2020 dataset,DS3-Net outperforms its supervised counterpart in each respect. Furthermore,with only 5% paired data, the proposed DS3-Net achieves competitive performancewith state-of-theart image translation methods utilizing 100% paired data,delivering an average SSIM of 0.8947 and an average PSNR of 23.60.

DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network