  Adversarial learning helps generative models translate MRI from source to
target sequence when lacking paired samples. However, implementing MRI
synthesis with adversarial learning in clinical settings is challenging due to
training instability and mode collapse. To address this issue, we leverage
intermediate sequences to estimate the common latent space among multi-sequence
MRI, enabling the reconstruction of distinct sequences from the common latent
space. We propose a generative model that compresses discrete representations
of each sequence to estimate the Gaussian distribution of vector-quantized
common (VQC) latent space between multiple sequences. Moreover, we improve the
latent space consistency with contrastive learning and increase model stability
by domain augmentation. Experiments using BraTS2021 dataset show that our
non-adversarial model outperforms other GAN-based methods, and VQC latent space
aids our model to achieve (1) anti-interference ability, which can eliminate
the effects of noise, bias fields, and artifacts, and (2) solid semantic
representation ability, with the potential of one-shot segmentation. Our code
is publicly available.
