DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
arxiv(2024)
Abstract
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
achieves improved reconstruction quality for 3D CMRA with reduced memory
burden.
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