A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers
A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.
Authors
- Iain Pierce (ORCID: https://orcid.org/0000-0001-8546-5023)
- Daniel S. Knight (ORCID: https://orcid.org/0000-0002-0512-3961)
- Karan Punjabi (ORCID: https://orcid.org/0000-0003-0361-0640)
- Jennifer Anne Steeden (ORCID: https://orcid.org/0000-0002-9792-2022)
- Alun David Hughes (ORCID: https://orcid.org/0000-0001-5432-5271)
- Vivek Muthurangu (ORCID: https://orcid.org/0000-0002-4292-6456)
- Rhodri Davies (ORCID: https://orcid.org/0000-0001-7630-7517)
- Tina Yao (ORCID: https://orcid.org/0000-0003-0470-311X)
- Yue Jiang (ORCID: https://orcid.org/0000-0002-9427-6627)
Institutions
- St Bartholomew's Hospital (GB)
- Energy Institute (GB)
- The Royal Free Hospital (GB)
- The London College (GB)
- University College London (GB)
Publication Details
- Journal
- Magnetic Resonance Imaging
- Published
- 2024-10-12
- DOI
- https://doi.org/10.1016/j.mri.2024.110253
- Citations
- 2
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- UK Research and Innovation
- British Heart Foundation