CineVN: Variational network reconstruction for rapid functional cardiac cine MRI
Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.
Authors
- Florian Knöll (ORCID: https://orcid.org/0000-0001-5357-8656)
- Daniel Giese (ORCID: https://orcid.org/0000-0002-3925-4441)
- Jens Wetzl (ORCID: https://orcid.org/0000-0002-6484-2130)
- Kelvin Chow (ORCID: https://orcid.org/0000-0003-0698-1746)
- Marc Vornehm (ORCID: https://orcid.org/0009-0004-5687-0563)
- Rizwan Ahmad (ORCID: https://orcid.org/0000-0002-5917-3788)
- Rolf Gebker (ORCID: https://orcid.org/0000-0002-9333-5381)
- Florian Fürnrohr (ORCID: https://orcid.org/0009-0001-8013-1320)
- Jianing Pang (ORCID: https://orcid.org/0000-0003-4522-4609)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- Siemens Healthcare (United States) (US)
- Siemens Healthineers (Germany) (DE)
- The Ohio State University (US)
- New York University (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1002/mrm.30260
- Citations
- 8
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- Siemens Medical Solutions USA
- Siemens Healthineers
- National Institute of Biomedical Imaging and Bioengineering