  Three-dimensional coronary magnetic resonance angiography (CMRA) demands
reconstruction algorithms that can significantly suppress the artifacts from a
heavily undersampled acquisition. While unrolling-based deep reconstruction
methods have achieved state-of-the-art performance on 2D image reconstruction,
their application to 3D reconstruction is hindered by the large amount of
memory needed to train an unrolled network. In this study, we propose a
memory-efficient deep compressed sensing method by employing a sparsifying
transform based on a pre-trained artifact estimation network. The motivation is
that the artifact image estimated by a well-trained network is sparse when the
input image is artifact-free, and less sparse when the input image is
artifact-affected. Thus, the artifact-estimation network can be used as an
inherent sparsifying transform. The proposed method, named De-Aliasing
Regularization based Compressed Sensing (DARCS), was compared with a
traditional compressed sensing method, de-aliasing generative adversarial
network (DAGAN), model-based deep learning (MoDL), and plug-and-play for
accelerations of 3D CMRA. The results demonstrate that the proposed method
improved the reconstruction quality relative to the compared methods by a large
margin. Furthermore, the proposed method well generalized for different
undersampling rates and noise levels. The memory usage of the proposed method
was only 63% of that needed by MoDL. In conclusion, the proposed method
achieves improved reconstruction quality for 3D CMRA with reduced memory
burden.
