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.

提出了一种名为混合融合生成对抗网络（HF-GAN）的新型统一框架，通过多模式磁共振成像，使得通过补充信息来提供可靠的临床应用诊断。实验证明，该方法在数据插补任务中表现出优异的性能，并且比现有方法在定量和定性比较方面更加优越。