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Abstract #0664

Rapid Reconstruction of Accelerated, Free-Breathing, Thoracic, Non-Contrast Magnetic Resonance Angiography using Convolutional Neural Network

Hassan Haji-valizadeh1,2, Daming Shen1,2, Florian A. Schiffers3, Oliver S. Schiffers3, and Daniel Kim1

1Department of Radiology, Northwestern University, Chicago, IL, United States, 2Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, 3Department of Computer Science & Engineering, Northwestern University, Evanston, IL, United States

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).

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