Multi-View Large Kernel Attention Network for Multi-Contrast MRI Volumetric Super-Resolution

Abstract

Deep learning–based multi-contrast Magnetic Resonance (MCMR) super-resolution (SR) has achieved notable success in accelerating image acquisition and improving image quality. However, significant challenges remain when dealing with the volumetric data: 1) Most existing MCMR SR methods primarily rely on single-slice information and fail to exploit high-dimensional volumetric contextual information; 2) Due to the sparsity of the original low-resolution volumetric data, conventional small kernel convolutions struggle to capture long-range contextual information. Although transformer-based approaches can model long-range dependencies, they suffer from high computational and memory demands when applied to high-dimensional volumetric data. To address these challenges, we propose a multi-view large-kernel attention network for MCMR volumetric SR. The method contains three stages: a cross-modality synthesis stage, an inter-slice deformable compensation stage, and a multi-view large-kernel attention fusion stage. Specifically, a multi-view fusion strategy is proposed to exploit the rich spatial contextual information inherent in high-dimensional volumetric data. A large-kernel convolution attention block is proposed to efficiently capture long-range dependencies from the sparsely sampled coronal and sagittal planes. By jointly integrating the high-order multi-view and multi-contrast information, our method successfully reconstructs high-quality MCMR volumetric data. Experimental results across different datasets, along with the downstream segmentation tasks, attest to the effectiveness of the proposed method.