MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach

MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach

Abstract Background We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. Methods We a...

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Bibliographic Details
Main Authors: Alexander Herold, Daniel Sobotka, Lucian Beer, Nina Bastati, Sarah Poetter-Lang, Michael Weber, Thomas Reiberger, Mattias Mandorfer, Georg Semmler, Benedikt Simbrunner, Barbara D. Wichtmann, Sami A. Ba-Ssalamah, Michael Trauner, Ahmed Ba-Ssalamah, Georg Langs
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:European Radiology Experimental
Subjects:
Deep learning
End-stage liver disease
Hypertension (portal)
Liver circulation
Magnetic resonance imaging
Online Access:https://doi.org/10.1186/s41747-025-00612-y
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