Dual branch diffusion-Mamba hybrid network for multi-scale MRI modality translation

Objective MRI modality translation aims to synthesize missing modalities from acquired scans. However, existing methods often struggle to preserve local details while maintaining global structural consistency. We propose a dual-branch diffusion-Mamba hybrid network (DBDM-Net) to address this challenge. Materials and methods DBDM-Net consists of a diffusion-based local branch for fine-detail reconstruction and a Mamba-based global branch for structural coherence preservation. A local feature extraction encoder enhances local representations, while a modulation block and a cross-scale fusion block enable modality-specific adaptation and multi-scale feature integration. Results DBDM-Net achieves competitive PSNR, SSIM, and perceptual quality compared with representative baseline methods. Discussion The proposed dual-branch framework effectively combines local detail reconstruction with global structural modeling, supporting its utility for MRI modality translation.