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Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis

机译：HI-NET：用于多模态MR图像合成的混合融合网络

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摘要

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts (i.e., modalities). Fusing this multi-modal data has proven particularly effective for boosting model performance in many tasks. However, due to poor data quality and frequent patient dropout, collecting all modalities for every patient remains a challenge. Medical image synthesis has been proposed as an effective solution, where any missing modalities are synthesized from the existing ones. In this paper, we propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis, which learns a mapping from multi-modal source images (i.e., existing modalities) to target images (i.e., missing modalities). In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality, and a fusion network is employed to learn the common latent representation of multi-modal data. Then, a multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality, acting as a generator to synthesize the target images. Moreover, a layer-wise multi-modal fusion strategy effectively exploits the correlations among multiple modalities, where a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical image synthesis methods.
机译：磁共振成像（MRI）是一种广泛使用的神经影像技术，可以提供不同对比度的图像（即，方式）。融合这种多模态数据，这些数据已经特别有效地在许多任务中提高了模型性能。然而，由于数据质量差和频繁的病人辍学，为每个患者收集所有方式仍然是一个挑战。已经提出了医学图像合成作为有效解决方案，其中任何缺失的方式都是从现有的解决方案中合成的。在本文中，我们提出了一种用于多模态MR图像合成的新型混合融合网络（HI-NET），其学习从多模态源图像（即，现有模式）到目标图像的映射（即，缺少模件） 。在我们的HI-NET中，利用模态特定的网络来学习每个单独的方式的表示，并且使用融合网络来学习多模态数据的共同潜在表示。然后，设计多模态综合网络以密集与每个模态的分层特征密集地组合潜伏表示，充当发电机以合成目标图像。此外，三层多模态融合策略有效利用多种模式之间的相关性，其中提出了混合融合块（MFB）以适自适应不同的融合策略。广泛的实验证明了所提出的模型优于其他最先进的医学图像合成方法。

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