Deep learning-based denoising image reconstruction of body magnetic resonance imaging in children
DL-based denoising image reconstruction of paediatric body MRI sequences employing radial k-space sampling allowed for improved overall image quality at shorter scan times. Respiratory motion artefacts were more pronounced on ungated T1-weighted images.
Authors
- Xinzeng Wang (ORCID: https://orcid.org/0000-0002-0415-7246)
- Maélène Lohézic
- Fraser M. Callaghan (ORCID: https://orcid.org/0000-0002-2862-6633)
- Christian J. Kellenberger (ORCID: https://orcid.org/0000-0001-9122-2431)
- Vanda Počepcová
- Julia Geiger (ORCID: https://orcid.org/0000-0003-3621-9955)
- Michael Zellner (ORCID: https://orcid.org/0000-0002-9118-7266)
Institutions
- University Children's Hospital Zurich (CH)
Publication Details
- Journal
- Pediatric Radiology
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1007/s00247-025-06230-5
- Citations
- 5
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- Universität Zürich