Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep conventional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully-sampled image. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care.

翻译：基于超极化惰性气体的磁共振成像（MRI）能够可视化人体肺部的结构与功能，但较长的成像时间限制了其广泛的研究与临床应用。深度学习通过从欠采样数据中重建图像，在加速MRI方面展现出巨大潜力。然而，现有深度卷积神经网络（CNN）大多将方形卷积直接应用于k空间数据，未考虑k空间采样的固有特性，制约了k空间学习效率与图像重建质量。本文提出一种编码增强型（EN2）复数CNN用于高度欠采样的肺部MRI重建。EN2采用沿频率或相位编码方向的一维卷积，模拟k空间采样机制，最大化利用k空间行或列内的编码相关性与完整性。同时引入复数卷积以从复数k空间数据中学习丰富表征。此外，开发特征增强模块化单元进一步提升重建性能。实验表明，该方法可从6倍欠采样的k空间数据中精确重建超极化129Xe和1H肺部MRI，并提供与全采样图像偏差最小的肺功能测量结果。这些结果验证了所提算法组件的有效性，并表明该方法可应用于研究与临床肺部疾病患者护理中的加速肺部MRI。