Abstract #4061

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

Nicole Wake^1,2, 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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.