Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography.

Journal: Skeletal radiology

PMID: 37191931

Abstract

OBJECTIVE: The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus.

Authors

Falko Ensle

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland. falko.ensle@usz.ch.
Malwina Kaniewska

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091, Zurich, Switzerland. malwina.kaniewska@usz.ch.
Anja Tiessen

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland.
Maelene Lohezic

Applications & Workflow, GE Healthcare, Manchester, UK.
Jonas M Getzmann

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, CH-8091, Zurich, Switzerland.
Roman Guggenberger

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.

Keywords

Adult Algorithms Brachial Plexus Deep Learning Female Humans Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Middle Aged Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (37191931)

Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography.

Abstract

Authors

Keywords

External Resources

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