Improving compressed sensing reconstructions for myocardial perfusion imaging with residual artifact learning
Ganesh Adluru1, Bradley D. Bolster, Jr.2, Edward DiBella1, and Brent Wilson3
1Radiology & Imaging Sciences, University of Utah, Salt lake city, UT, United States, 2US MR R&D Collaborations, Siemens Healthineers, Salt Lake City, UT, United States, 3Cardiology, University of Utah, Salt Lake City, UT, United States
Compressed sensing/constrained reconstruction methods have been successfully applied to myocardial perfusion imaging for improving in-plane resolution and improving slice coverage without losing temporal resolution. However at high acceleration factors and in the presence of large inter-time frame motion image quality from the CS methods is affected. Here we propose an artifact learning neural network that aims to improve the image quality of spatio-temporal constrained reconstruction methods for gated Cartesian and ungated radial data. Promising results are shown on datasets that were not used in training the neural network.
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