TransGRAPPA: Self-supervised Transformer Network for k-Space Interpolation

Motivation: Addressing limitations in parallel imaging, particularly GRAPPA’s challenges like noise amplification and dependence on linear k-space value combinations. Goal(s): To enhance k-space interpolation accuracy with a transformer network, thereby improving the quality of clinical imaging. Approach: We employ a novel self-supervised transformer network with an attention mechanism - TransGRAPPA, exploiting latent features for nonlinear interpolation of missing k-space points. Results: TransGRAPPA outperforms GRAPPA and RAKI in terms of NRMSE, PSNR, SSIM, and noise reduction, showcasing enhanced capabilities on fastMRI’s multi-coil knee dataset. Impact: The study presents a innovative reconstruction method using transformer network to explore k-space point relationships with limited training data, offering potential improvements in MR image quality and scan speed, and more efficient and accurate diagnostics in medical imaging.