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Abstract #1129

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

Timothy Allen1,2, Leah C Henze Bancroft2, Lloyd Estkowski3, Ty A Cashen3, Frederick Kelcz2, Frank R Korosec1,2, Roberta M Strigel1,2,4, Orhan Unal1,2, 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.

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