We went below the MRI acceleration factors (a.k.a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered powerful deep learning based image enhancement methods to compensate for the underresolved images. We thoroughly study the influence of the sampling patterns, the undersampling and the downscaling factors, as well as the recovery models on the final image quality for both the brain and the knee fastMRI benchmarks. The quality of the reconstructed images surpasses that of the other methods, yielding an MSE of 0.00114, a PSNR of 29.6 dB, and an SSIM of 0.956 at x16 acceleration factor. More extreme undersampling factors of x32 and x64 are also investigated, holding promise for certain clinical applications such as computer-assisted surgery or radiation planning. We survey 5 expert radiologists to assess 100 pairs of images and show that the recovered undersampled images statistically preserve their diagnostic value.

中文翻译：

---

通过极限k空间欠采样和超分辨率实现超快MRI

我们低于所有引用原始fastMRI挑战的已发表论文所报告的MRI加速因子（又名k空间欠采样），然后考虑了基于强大的深度学习的图像增强方法来补偿欠分辨率图像。我们彻底研究了采样模式，欠采样和缩小因子以及恢复模型对大脑和膝盖fastMRI基准的最终图像质量的影响。重建图像的质量优于其他方法，在x16加速因子下产生的MSE为0.00114，PSNR为29.6 dB，SSIM为0.956。还对x32和x64的更极端的欠采样因子进行了研究，为某些临床应用（如计算机辅助手术或放射规划）提供了希望。