3D whole-heart coronary MR angiography (CMRA) has shown great potential to visualize the coronary arteries. However, scan times remain lengthy as a large amount of data needs to be acquired to obtain high-resolution images. Several undersampled compressed-sensing (CS) reconstruction approaches have been applied to accelerate CMRA. However, CS-based techniques suffer from residual aliasing artifacts, high-dependency on regularization parameters and long reconstruction times. We propose a Variational Neural Network for fast reconstruction of undersampled motion-compensated 3D cardiac MRI, which combined with 100% respiratory efficiency, enables the acquisition of high-quality isotropic CMRA images in ~2-4 minutes, and their reconstruction in ~20 seconds.
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