Meeting Banner
Abstract #0098

Improving diagnostic confidence using a Deep-Learning Reconstructed Fast Motion-Robust PROPELLER protocol for Shoulder Imaging

Laura Carretero1,2, Xinzeng Wang3, Eugenia Sánchez4, Satish Nagrani4, Daniel Litwiller5, Pablo García-Polo1, Maggie Fung6, and Mario Padrón4
1GE Healthcare, Madrid, Spain, 2Rey Juan Carlos University, Madrid, Spain, 3GE Healthcare, Houston, TX, United States, 4ClĂ­nica Cemtro, Madrid, Spain, 5GE Healthcare, Colorado, CO, United States, 6GE Healthcare, New York, NY, United States


PROPELLER imaging is the choice for motion-prone anatomies, to generate good diagnostic quality images even for challenging patients. However, it is associated with prolonged scan times. In this study we evaluate the application of a new DL-based reconstruction algorithm to enhance a fast PROPELLER shoulder protocol with the goal of providing consistent diagnostic confidence and an overall improved image quality compared to conventional routine protocols. Our study demonstrates the proposed under 10min shoulder MRI protocols are interchangeable and have the same diagnostic confidence with better SNR and lesion conspicuity than routine MRI protocols for shoulder post-contrast and instability exams.

This abstract and the presentation materials are available to members only; a login is required.

Join Here