Keywords: Image Reconstruction, CardiovascularA deep learning reconstruction framework, trained in an end-to-end fashion and incorporating both a non-rigid respiratory motion estimation network and a motion-informed model-based reconstruction network, has been previously demonstrated to enable good quality images from seven-fold undersampled acquisitions for coronary magnetic resonance angiography applications. Herein, we apply the framework to whole-heart MRI scans of patients with congenital heart disease, enabling fast reconstruction of 7×-accelerated acquisitions and achieving image quality comparable to that of state-of-the-art patch-based low-rank iterative techniques.
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