  Multisequence Magnetic Resonance Imaging (MRI) provides a reliable diagnosis
in clinical applications through complementary information within sequences.
However, in practice, the absence of certain MR sequences is a common problem
that can lead to inconsistent analysis results. In this work, we propose a
novel unified framework for synthesizing multisequence MR images, called Hybrid
Fusion GAN (HF-GAN). We introduce a hybrid fusion encoder designed to ensure
the disentangled extraction of complementary and modality-specific information,
along with a channel attention-based feature fusion module that integrates the
features into a common latent space handling the complexity from combinations
of accessible MR sequences. Common feature representations are transformed into
a target latent space via the modality infuser to synthesize missing MR
sequences. We have performed experiments on multisequence brain MRI datasets
from healthy individuals and patients diagnosed with brain tumors. Experimental
results show that our method outperforms state-of-the-art methods in both
quantitative and qualitative comparisons. In addition, a detailed analysis of
our framework demonstrates the superiority of our designed modules and their
effectiveness for use in data imputation tasks.
