MD-GraphFormer: A Model-Driven Graph Transformer for Fast Multi-Contrast MR Imaging

Jiazhen Wang, Yan Yang, Heran Yang, Chunfeng Lian, Zongben Xu, Jian Sun

Abstract: In magnetic resonance imaging (MRI), multi-contrast pulse sequences are routinely acquired, providing complementary information for medical diagnosis. Compared with the single-contrast MR image reconstruction, the multi-contrast MR imaging could further accelerate data acquisition and improve reconstruction quality by leveraging the complementary information of multi-contrast MR images. In this paper, we propose a model-driven graph transformer (MD-GraphFormer) for fast multi-contrast MR imaging, which incorporates the physical constraints of MRI and investigates the complementary information among multi-contrast MR images using graph structure and attention mechanism. The MD-GraphFormer consists of graph-attention-based interaction modules (GAB-IM) and multi-contrast data consistency modules (MC-DCM). GAB-IM learns and interacts the features of multi-contrast MR images over the graph with nodes representing MR contrasts. MC-DCM enforces the consistency between the reconstructed multi-contrast MR images and their corresponding measurements in k-space. Extensive experiments are conducted on the collected raw uMR and SMS brain MRI datasets under different sampling patterns and sampling rates. The results demonstrate that the proposed MD-GraphFormer outperforms the previous multi-contrast MRI reconstruction methods in multi-coil imaging settings.