  Multi-modal brain images from MRI scans are widely used in clinical diagnosis
to provide complementary information from different modalities. However,
obtaining fully paired multi-modal images in practice is challenging due to
various factors, such as time, cost, and artifacts, resulting in
modality-missing brain images. To address this problem, unsupervised
multi-modal brain image translation has been extensively studied. Existing
methods suffer from the problem of brain tumor deformation during translation,
as they fail to focus on the tumor areas when translating the whole images. In
this paper, we propose an unsupervised tumor-aware distillation teacher-student
network called UTAD-Net, which is capable of perceiving and translating tumor
areas precisely. Specifically, our model consists of two parts: a teacher
network and a student network. The teacher network learns an end-to-end mapping
from source to target modality using unpaired images and corresponding tumor
masks first. Then, the translation knowledge is distilled into the student
network, enabling it to generate more realistic tumor areas and whole images
without masks. Experiments show that our model achieves competitive performance
on both quantitative and qualitative evaluations of image quality compared with
state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the
generated images on downstream segmentation tasks. Our code is available at
https://github.com/scut-HC/UTAD-Net.
