  Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance
imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially
gliomas. In clinical practice, common MRI modalities such as T1, T2, and fluid
attenuation inversion recovery are relatively easy to access while T1ce is more
challenging considering the additional cost and potential risk of allergies to
the contrast agent. Therefore, it is of great clinical necessity to develop a
method to synthesize T1ce from other common modalities. Current paired image
translation methods typically have the issue of requiring a large amount of
paired data and do not focus on specific regions of interest, e.g., the tumor
region, in the synthesization process. To address these issues, we propose a
Difficulty-perceived common-to-T1ce Semi-Supervised multimodal MRI Synthesis
network (DS3-Net), involving both paired and unpaired data together with
dual-level knowledge distillation. DS3-Net predicts a difficulty map to
progressively promote the synthesis task. Specifically, a pixelwise constraint
and a patchwise contrastive constraint are guided by the predicted difficulty
map. 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 performance
with state-of-theart image translation methods utilizing 100% paired data,
delivering an average SSIM of 0.8947 and an average PSNR of 23.60.
