Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI.
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| Title: | Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI. |
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| Authors: | Nedelcu A; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany. andrea.nedelcu@uniklinik-freiburg.de., Russe MF; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany., Wilpert C; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany., Oerther B; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany., Nickel DM; MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany., Strecker R; EMEA Scientific Partnerships, Siemens Healthineers AG, Forchheim, Germany., Bamberg F; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany., Weiß J; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany., Engel H; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, University of Freiburg, Freiburg im Breisgau, Germany. |
| Source: | European radiology experimental [Eur Radiol Exp] 2026 Jun 25; Vol. 10 (1). Date of Electronic Publication: 2026 Jun 25. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: SpringerOpen Country of Publication: England NLM ID: 101721752 Publication Model: Electronic Cited Medium: Internet ISSN: 2509-9280 (Electronic) Linking ISSN: 25099280 NLM ISO Abbreviation: Eur Radiol Exp Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2509-9280 |
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| DOI: | 10.1186/s41747-026-00758-3 |
