In this study we developed a convolutional neural network (CNN) for reconstructing 3D non-contrast magnetic resonance angiography (NC-MRA) images. We trained our proposed CNN using 4,800 zero-filled images and the corresponding GRASP reconstructed images from 10 patients as input and output, respectively. For validation, we used 6,720 zero-filled images from 14 patients as input to our trained CNN. Comparison between CNN and GRASP reconstructions showed excellent agreement using quantitative metrics and quantified aortic diameters . The mean reconstruction time, excluding the pre- and post-processing steps, for CNN (74 s) was 99% shorter than GRASP (12,703 s).