Abstract #3810

Automated image prescription for liver MRE using an AI method trained without manual labeling

Garrett Fullerton^1,2, Collin J Buelo^1,2, Dan Rettmann³, Arnaud Guidon³, Scott B Reeder^1,2,4,5,6, Diego Hernando^1,2,4,7, and Jitka Starekova¹

¹Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States, ³GE HealthCare, Waukesha, WI, United States, ⁴Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States, ⁵Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States, ⁶Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States, ⁷Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Other AI/ML, Machine Learning/Artificial Intelligence

Motivation: MRE is a reliable, quantitative method for the assessment and staging of liver fibrosis. The standard manual MRE image prescription requires proper placement over the liver to ensure consistent MRE quantification. Scan positioning is relatively time-consuming and prone to error and inconsistency.

Goal(s): To develop and implement an automated methodology for MRE prescription from localizers, trained entirely from technologist-prescribed clinical exams.

Approach: Extracted MRE scan coordinates from 354 clinical exams and trained a YOLOv8-nano object detection network to predict prescription planes from a multi-plane localizer series.

Results: We successfully developed a method for automated MRE prescription with implementation on a clinical MRI system.

Impact: Automatic image plan prescription for MRE can minimize technologist-dependent planning errors and scan inconsistency. This may lead to subsequent improvements in both the value and reproducibility of MRE as a quantitative biomarker of liver fibrosis.

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