Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis

TL;DR

This paper introduces Hi-Net, a hybrid-fusion network that synthesizes missing MRI modalities from existing ones by learning shared and modality-specific representations, improving multi-modal image synthesis accuracy.

Contribution

The paper proposes a novel hybrid-fusion network with a layer-wise multi-modal fusion strategy and Mixed Fusion Block for adaptive modality integration in MRI synthesis.

Findings

Outperforms state-of-the-art synthesis methods

Effectively exploits correlations among multiple modalities

Improves accuracy of missing modality generation

Abstract

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 to this, 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 is presented to effectively exploit the correlations among multiple modalities, in which a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies (i.e., element-wise summation, product, and maximization). Extensive experiments demonstrate that the proposed model outperforms other state-of-the-art medical image synthesis methods.