Although dynamic contrast enhanced MRI has been successfully applied for characterizing coronary artery diseases, an acquisition scheme limited to 2-4 short axis slices restricts coverage of the left ventricle. Radial simultaneous multi-slice has been shown to improve DCE cardiac perfusion by providing complete coverage of the left ventricle but also requires an increase in reconstruction time. Here we propose using a modified Unet with a residual artifact learning framework to improve reconstruction time and image quality of spatio-temporal constrained reconstruction methods for radial SMS datasets. Results demonstrate promising improvements with a speed up in reconstruction by a factor of ~150.
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