CINE MRI is the gold-standard for the assessment of cardiac function. Compressed Sensing (CS) reconstruction has enabled 3D CINE acquisition with left ventricular (LV) coverage in a single breath-hold. However, maximal achievable acceleration is limited by the performance of the selected reconstruction method. Deep learning has shown to provide good-quality reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D+time) reconstruction network for prospectively undersampled 3D Cartesian cardiac CINE that utilizes complex-valued spatial-temporal convolutions. The proposed network outperforms CS in visual quality and shows good agreement for LV function to gold-standard 2D CINE.
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