Integrating AI-assisted image enhancement with physics-based synthesis of low-field MRI from high-field MRI.

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Abstract

Low-field magnetic resonance imaging (MRI) offers distinct advantages in terms of affordability, portability, and accessibility. However, its widespread adoption is limited by an inherently low signal-to-noise ratio and reduced spatial resolution. This study proposes an AI-assisted framework to enhance low-field MRI image quality and overcome these limitations. We propose a two-stage framework to generate high-quality low-field MRI images. In the first stage, realistic low-field images are synthesized from high-field acquisitions using a physics-informed forward model that incorporates spiral k-space trajectories and accounts for nonlinear magnetic field gradients, B0 inhomogeneity, k-space undersampling, and image reconstruction characteristics of low-field systems. In the second stage, a 3D U-Net enhanced with a multi-head attention in a Vision Transformer (ViT) module is trained on paired synthetic low-and high-field images to serve as a post processing following conventional image reconstruction. On the synthetic test set, our framework demonstrates strong performance, achieving a peak signal-to-noise ratio (PSNR) of 19.08 ± 2.85 dB for the baseline U-Net model and 21.00 ± 2.50 dB with the ViT block, demonstrating high reconstruction fidelity. The structural similarity index measure (SSIM) reaches 0.6456 ± 0.0779 (without ViT) and 0.6639 ± 0.0798 (with ViT), along with low normalized root mean squared error (NRMSE) values of 0.3866 ± 0.0952 and 0.3084 ± 0.0695, respectively. These results highlight significant improvements in both image quality and reconstruction robustness. The trained network, applied as a post-processing step after conventional reconstruction, consistently enhances the contrast-to-noise ratio (CNR) of the output images, supporting the qualitative observations of improved image contrast and clarity. The proposed framework addresses key limitations hindering the broader adoption of low-field MRI, including noise, artifacts, and resolution loss inherent to low-field acquisitions. By integrating deep learning with physics-based simulations, the approach achieves notable qualitative and quantitative enhancements in denoising, artifact removal, and overall image quality. These results highlight the framework's potential to improve the practical utility of low-field MRI substantially.

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