Meeting Banner
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

Synopsis

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.

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.

Keywords