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Abstract #4061

Deep Learning Reconstruction of 3D Zero Echo Time Magnetic Resonance Images for the Creation of 3D Printed Anatomic Models

Nicole Wake1,2, Stephanie Shamir1, Beverly Thornhill1, Nogah Haramati1, Graeme McKinnon3, Mathias Engstrom4, Florian Wiesinger4, Michael Carl5, Fraser Robb6, and Maggie Fung7
1Department of Radiology, Montefiore Medical Center, Bronx, NY, United States, 2Center for Advanced Imaging Innovation and Research, Department of Radiology, NYU Langone Health, New York, NY, United States, 3GE Healthcare, Waukesha, WI, United States, 4GE Healthcare, Munich, Germany, 5GE Healthcare, San Diego, CA, United States, 6GE Healthcare, Aurora, OH, United States, 7GE Healthcare, New York, NY, United States

Patient-specific three-dimensional (3D) printed anatomic models are valuable clinical tools which are generally created from computed tomography (CT). However, magnetic resonance imaging (MRI) is an attractive alternative, since it offers exquisite soft-tissue characterization and flexible image contrast mechanisms while avoiding the use of ionizing radiation. The purpose of this study was to evaluate the image quality and assess the feasibility of creating 3D printed models using a 3D Zero echo time (ZTE) MR images which were reconstructed with a deep learning reconstruction method.

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