Abstract #0003

Automated MR Image Prescription of the Liver using Deep Learning: Development, Evaluation & Prospective Implementation

Ruiqi Geng¹, Collin J. Buelo¹, Mahalakshmi Sundaresan², Jitka Starekova³, Nikolaos Panagiotopoulos^3,4, Thekla Helene Oechtering^3,4, Edward M. Lawrence³, Marcin Ignaciuk³, Scott B Reeder⁵, and Diego Hernando⁶

¹Departments of Medical Physics, Radiology, University of Wisconsin, Madison, Madison, WI, United States, ²Department of Electrical and Computer Engineering, University of Wisconsin, Madison, Madison, WI, United States, ³Department of Radiology, University of Wisconsin, Madison, Madison, WI, United States, ⁴Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany, ⁵Departments of Medical Physics, Radiology, Medicine, Emergency Medicine, Biomedical Engineering, University of Wisconsin, Madison, Madison, WI, United States, ⁶Departments of Medical Physics, Radiology, Electrical and Computer Engineering, Biomedical Engineering, University of Wisconsin, Madison, Madison, WI, United States

Synopsis

Keywords: YIA, LiverThis work developed a novel automated AI-based method for liver image prescription from a localizer and evaluated it in a large retrospective patient cohort (1,039 patients for training/testing), across pathologies, field strengths, and against radiologists’ inter-reader reproducibility performance. AI-based 3D axial prescription achieved a S/I shift of <2.3 cm compared to manual prescription for 99.5% of test dataset. The AI method performed well across all sub-cohorts and better in 3D axial prescription than radiologists’ inter-reader reproducibility performance. We successfully implemented the AI method on a clinical MR system, which demonstrated robust performance across localizer sequences.

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