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Abstract #0919

Rapid and High Resolution Pelvic MRI Using Deep Learning Reconstruction

Melany B Atkins1, Arnaud Guidon2, Michael Vinski3, Thomas Schrack4, Heidi Harris5, and Ersin Bayram6
1Radiology, Fairfax Radiological Consultants, Arlington, VA, United States, 2GE Healthcare, Boston, MA, United States, 3GE Healthcare, Lynchburg, VA, United States, 4Fairfax Radiological Consultants, Fairfax, VA, United States, 5GE Healthcare, Waukesha, WI, United States, 6GE Healthcare, Houston, TX, United States


MRI plays an important role in pelvic assessment. For instance, it is the modality of choice for rectal cancer staging, gynecologic cancer staging, uterine fibroid evaluation and ovarian tumor characterization. Due to the complex nature of the anatomy and clinical demands of these protocols, high resolution thin slice volumetric scans are desired but low SNR, prolonged scan times and motion artifacts remain problematic. In this work, we deploy a combination of recent technical advances in particular high density flexible coils, compressed sensing, and deep learning reconstruction to tackle these challenges and report our feasibility results.

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