A Transformer-based framework for non-Cartesian MRI reconstruction

Non-Cartesian reconstruction is a crucial technique for accelerating MRI. However, traditional non-Cartesian reconstruction algorithms often result in suboptimal image quality. Recently, deep neural networks have emerged as powerful tools for MRI reconstruction, yet their application to non-Cartesian acquisitions remains underexplored. Transformer-based approaches have shown impressive performance in image super-resolution, prompting us to explore their potential in this domain. To tackle these challenges, this paper introduces a novel framework that combines non-Cartesian image reconstruction techniques with a Transformer-based network. The proposed framework comprises nonuniform Fourier transform, image feature extraction, and image reconstruction modules. To assess the effectiveness of our approach, we performed experiments using the single-coil knee dataset from fastMRI. Compared to other methods, our proposed approach demonstrated a 2.024 dB improvement in PSNR and a 0.117 increase in SSIM under a 4x accelerated radial undersampling condition.