Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, coronary magnetic resonance angiography, compressed sensing, deep learning
Motivation: While classical non-learning reconstruction methods for 3D coronary magnetic resonance angiography (CMRA) lack a task-adaptive image prior, 3D deep unrolling suffers from a low memory efficiency, causing a reduced number of iterations and a compromised image quality.
Goal(s): We aim to combine compressed sensing and deep learning regularization by using a trained de-aliasing network as the sparsifying transform.
Approach: We compared the method with PROST, Plug-and-Play, DAGAN, and MoDL for accelerating CMRA in 20 healthy subjects.
Results: Visual inspections and quantitative comparisons both found a substantially improved reconstruction quality from DARCS relative to the other methods.
Impact: The proposed method overcomes an important limitation of 3D unrolling while maintaining its core advantage of task-adaptive regularization. The method not only can accelerate 3D CMRA, but also has the potential for general 3D image reconstructions.
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