Keywords: AI/ML Image Reconstruction, Image Reconstruction, Self-supervised learning
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.
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