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Abstract #2320

Shortening Diagnostic T2w Breast Protocols to Capitalize on the Benefits of a Deep Learning Reconstruction 

Timothy J Allen1, Leah C Henze Bancroft2, Roberta M Strigel1,2,3, Ty Cashen4, Orhan Unal2, Frank R Korosec1,2, Ping Wang1, Lloyd Estkowski4, Fred Kelcz2, Amy M Fowler1,2,3, R Marc Lebel4, and James Holmes2
1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, United States, 4Global MR Applications and Workflow, GE Healthcare, Madison, WI, United States

A SSFSE-based fast acquisition was used in conjunction with a vendor-supplied, deep learning-based reconstruction to obtain T2-weighted images (Rapid T2w+DL) for diagnostic breast MRI. Radiologists compared the Rapid T2w+DL images to standard-of-care T2w FSE images assessing both image quality and utility in completing a typical clinical task. The fast acquisition provided a reduction in average scan time from 3:35 (min:sec) to 1:16 (64%). Deep learning was used to alleviate issues associated with the fast protocol (e.g. blurring and reduced SNR). Rapid T2w+DL images allowed for completion of the clinical task with results comparable to those obtained with standard T2w images.

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