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.

翻译：多序列磁共振成像（MRI）通过序列间的互补信息为临床应用提供可靠诊断。然而，实践中特定MR序列的缺失是常见问题，可能导致分析结果不一致。本研究提出一种新颖的多序列MR图像合成统一框架，称为混合融合生成对抗网络（HF-GAN）。我们设计了混合融合编码器，确保互补信息与模态特定信息的解耦提取；同时引入基于通道注意力的特征融合模块，将特征整合至公共潜在空间，以处理可获取MR序列组合的复杂性。通过模态注入器将公共特征表示转换至目标潜在空间，从而合成缺失的MR序列。我们在健康个体和脑肿瘤患者的多序列脑部MRI数据集上进行了实验。结果表明，本方法在定量与定性比较中均优于现有先进方法。此外，对框架的详细分析验证了所设计模块的优越性及其在数据填补任务中的有效性。