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Abstract #1750

Clinical Validation of Deep-learning Approach to Estimate Proton-Density Fat Fraction from In-phase and Out-of-phase T1-weighted MRI

Mehmet Can Yavuz1, David T. Harris2, Scott B. Reeder2, Claude B. B. Sirlin3, Yang Yang1, and Kang Wang1
1Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Department of Radiology, UC San Diego, San Diego, CA, United States

Synopsis

Keywords: Liver, Machine Learning/Artificial Intelligence, Fat, Data Analysis.

Motivation: Accurate liver fat quantification is vital for diagnosing Metabolic-associated Steatohepatitis (MASH). Conventional IOP MRI faces limitations, and advanced CSE methods are not widely available.

Goal(s): Evaluate AI-IOP PDFF externally, compare its accuracy with 2-point Dixon SFF, and identify factors influencing PDFF estimation for model improvement.

Approach: Analyzed 526 liver MRIs with CSE-MRI and T1w-IOP sequences. Generated CSE-PDFF, Dixon SFF, and AI-IOP PDFF maps using automated segmentation and statistical analyses.

Results: AI-IOP PDFF closely matched CSE-PDFF and outperformed Dixon SFF in accuracy. External validation confirmed its reliability across settings.

Impact: AI-IOP enables reliable liver fat quantification with common T1w-IOP sequences, enhancing MASH diagnosis where advanced CSE methods are unavailable.

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Keywords