Abstract #0533

Automated Image Prescription for Liver MRI using Deep Learning

Ruiqi Geng^1,2, Mahalakshmi Sundaresan³, Jitka Starekova¹, Collin J Buelo^1,2, Nikolaos Panagiotopoulos¹, Marcin Ignaciuk¹, Thekla Helene Oechtering¹, Scott B Reeder^1,2,4,5,6, and Diego Hernando^1,2

¹Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁴Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁵Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁶Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States

To enable fully free-breathing, single button-push liver exams, an automated AI-based method for image prescription of liver MRI was developed and evaluated. A total of seven classes of rectangular bounding boxes covering the liver, torso, and arms for each localizer orientation were manually and automatically labeled to enable 3D prescription in any orientation. The intersection over union (IoU) between manual and automated 2D liver detection had a median > 0.88 and interquartile range < 0.11 for all classes. The shift in the resultant 3D axial prescription was less than 9 mm in S/I dimension for 91% of the test dataset.

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