Multisequence Magnetic Resonance Imaging (MRI) provides a more reliable diagnosis in clinical applications through complementary information across 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). The fundamental mechanism of this work is principled feature disentanglement, which aligns the design of the architecture with the complexity of the features. A powerful many-to-one stream is constructed for the extraction of complex complementary features, while utilizing parallel, one-to-one streams to process modality-specific information. These disentangled features are dynamically integrated into a common latent space by a channel attention-based fusion module (CAFF) and then transformed via a modality infuser to generate the target sequence. We validated our framework on public datasets of both healthy and pathological brain MRI. Quantitative and qualitative results show that HF-GAN achieves state-of-the-art performance, with our 2D slice-based framework notably outperforming a leading 3D volumetric model. Furthermore, the utilization of HF-GAN for data imputation substantially improves the performance of the downstream brain tumor segmentation task, demonstrating its clinical relevance.

翻译：多序列磁共振成像（MRI）通过序列间的互补信息，在临床应用中提供了更可靠的诊断依据。然而在实际操作中，某些MR序列的缺失是常见问题，可能导致分析结果不一致。本研究提出了一种新颖的统一框架用于合成多序列MR图像，称为混合融合生成对抗网络（HF-GAN）。该工作的核心机制是原则性特征解耦，使架构设计与特征复杂性相匹配。我们构建了强大的多对一流以提取复杂的互补特征，同时利用并行的单对一流处理模态特定信息。这些解耦特征通过基于通道注意力的融合模块（CAFF）动态整合到公共潜在空间中，随后经由模态注入器转换生成目标序列。我们在健康与病理脑部MRI的公共数据集上验证了该框架。定量与定性结果表明，HF-GAN实现了最先进的性能，其中我们基于二维切片的框架显著优于领先的三维体积模型。此外，利用HF-GAN进行数据填补可显著提升下游脑肿瘤分割任务的性能，这证明了其临床相关性。