This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, very few low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of Fourier transforms of the subsampled and fully sampled k-space data. Numerous experiments show the remarkable performance of the proposed method; only 29% of k-space data can generate images of high quality as effectively as standard MRI reconstruction with fully sampled data.

本文利用深度学习方法，通过对k空间数据进行次-Nyquist采样策略降维，来提高磁共振成像的速度，并提供了理由为什么该方法表现良好。通过在时间消耗方向上采用均匀子采样捕捉高分辨率图像信息，同时允许由Poisson求和公式指导的图像折叠问题。为了处理由图像折叠产生的定位不确定性，只添加了极少量的低频k-空间数据。大量的实验证明了该方法的显着性能表现，只需要用到29%的k-空间数据就可以像使用完全采样数据的标准MRI重建一样高效生成高质量的图像。

基于深度学习的低采样MRI重建