Abstract #0091

Multicenter, Multivendor Development and Evaluation of Automated Liver MR Image Prescription

Garrett C. Fullerton^1,2, Jitka Starekova¹, Collin J. Buelo^1,2, David T. Harris¹, Raphael do Vale Souza¹, Aaron Faacks¹, Alexandra Anagnostopoulos¹, Austin Murphy¹, Norah Duritsa¹, Eashna Agarwal¹, Lukas Müller¹, Diana Kadi³, Takeshi Yokoo⁴, Mustafa R. Bashir^3,5,6, Scott B. Reeder^1,2,7,8,9, and Diego Hernando^1,2,7,10

¹Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³Department of Radiology, Duke University Medical Center, Durham, NC, United States, ⁴Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States, ⁵Department of Medicine, Duke University Medical Center, Durham, NC, United States, ⁶Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, United States, ⁷Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁸Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁹Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States, ¹⁰Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Liver, MR Value

Motivation: Manual image prescription is susceptible to human error, limiting efficiency and degrading prescription reproducibility. Previous work addressing this relies on single-center, single-vendor datasets.

Goal(s): Extend previous work on automated liver MR image prescription, now with performance validation across vendors, center, field strength, and known liver pathology.

Approach: Train and evaluate a YOLOv8 model on a large manually curated and annotated dataset (11012 total; 7707 training, 1102 validation, 2203 test) spanning a wide variety of acquisition and patient characteristics that may impact performance.

Results: Our model was successful in prescribing liver MR volumes with high performance across vendors, field strength, and known liver pathology.

Impact: The validated multicenter, multivendor liver prescription model may improve liver MRI workflow efficiency and reproducibility. Further, the trained models will be made available upon publication, providing an important resource for research and clinical MR applications in the liver.

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