Deep learning-based chemical shift-artifact correction of ZTE MRI for enhanced bone depiction of the lumbar spine.

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Bibliographic Details
Title: Deep learning-based chemical shift-artifact correction of ZTE MRI for enhanced bone depiction of the lumbar spine.
Authors: Obermüller C; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. carina.obermueller@usz.ch., Bach U; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Zecca F; Department of Medical Imaging, Oncopole Claudius Regaud - IUCT Oncopole, Toulouse, France., Heidt F; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Lohezic M; GE HealthCare, Zurich, Switzerland., Mandava S; GE HealthCare, Atlanta, GA, USA., de Arcos Rodriguez J; GE HealthCare, Valencia, Spain., Wiesinger F; GE HealthCare, Munich, Germany., Guggenberger R; Department of Radiology and Nuclear Medicine, Cantonal Hospital Winterthur, University of Zurich, Zurich, Switzerland., Burian E; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Kroschke J; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland., Ensle F; Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Source: Skeletal radiology [Skeletal Radiol] 2026 Jul 06. Date of Electronic Publication: 2026 Jul 06.
Publication Type: Journal Article
Journal Info: Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7701953 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-2161 (Electronic) Linking ISSN: 03642348 NLM ISO Abbreviation: Skeletal Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-2161
DOI:10.1007/s00256-026-05290-4