Abstract #1129

Feasibility of Using a Deep Learning Reconstruction to Increase Protocol Flexibility for Breast MRI

Timothy Allen^1,2, Leah C Henze Bancroft², Lloyd Estkowski³, Ty A Cashen³, Frederick Kelcz², Frank R Korosec^1,2, Roberta M Strigel^1,2,4, Orhan Unal^1,2, and James H Holmes²

¹Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Global MR Applications and Workflow, GE Healthcare, Madison, WI, United States, ⁴Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, United States

A deep learning reconstruction was evaluated for use in T2w breast MRI. Breast radiologists scored deep learning (DL) images significantly higher than non-DL images in four categories: artifacts, perceived signal-to-noise ratio, sharpness, and overall quality. DL was then used to improve the quality of high-resolution T2w breast series acquired in clinically-acceptable scan times. High resolution protocols typically require compromise between scan time and image quality. However, implementation of a deep learning reconstruction allowed for shorter scan times while maintaining diagnostic image quality. A deep learning reconstruction could allow for a clinically-feasible, high-resolution T2w acquisition.

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.