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Abstract #4335

Towards Fully Automated Breast MR Exams using Deep Learning

Kang Wang1, Dawei Gui2, James Holmes3, Alan McMillan3, Leah Henze Bancroft3, Roberta Strigel3,4,5, Frank Korosec3,4, and Ersin Bayram6

1Global MR Applications & Workflow, GE Healthcare, Madison, WI, United States, 2MR Engineering, GE Healthcare, Waukesha, WI, United States, 3Radiology, University of Wisconsin-Madison, Madison, WI, United States, 4Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 5Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, United States, 6Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States

Breast MR exams can be challenging for inexperienced MR technologists. For example, breast MRI typically requires the prescription of two carefully positioned and sized shim volumes, one for each breast, to improve the local B0 homogeneity and fat suppression. Normally, this procedure is performed manually, which requires an experienced MR technologist and can be challenging for new technologists. The goal of this project is to use deep learning to automate breast MR prescription, including placing the two shim volumes and imaging volumes automatically, to improve breast MR prescription consistency, quality, and to shorten the exam time.

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