A Unified Framework for Synthesizing Multisequence Brain MRI via Hybrid Fusion

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.