  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.
