Prospective Respiratory Motion Correction Using Machine Learning and Pilot Tone ( <scp>PROMPT</scp> ) in Cardiac <scp>MRI</scp>
The proposed respiratory motion correction approach was successfully implemented. The LSTM-based predictive model outperformed linear and polynomial regression models. In LGE imaging, PROMPT significantly reduced in-plane motion and showed potential for limiting through-plane motion compared to the clinical protocol.
Authors
- Orlando P. Simonetti (ORCID: https://orcid.org/0000-0002-8994-0095)
- Kelvin Chow (ORCID: https://orcid.org/0000-0003-0698-1746)
- Peter Speier
- Rizwan Ahmad (ORCID: https://orcid.org/0000-0002-5917-3788)
- Matthew Tong (ORCID: https://orcid.org/0000-0003-2482-1324)
- Mario Bacher (ORCID: https://orcid.org/0000-0002-5529-3598)
- Yuchi Han (ORCID: https://orcid.org/0000-0001-7582-1848)
- Ning Jin
- Yue Pan (ORCID: https://orcid.org/0000-0001-5690-0137)
- Vedat Yildiz
Institutions
- The Ohio State University Wexner Medical Center (US)
- Medical Solutions (GB)
- Siemens Healthineers (Germany) (DE)
- The Ohio State University (US)
- University of Lausanne (CH)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1002/mrm.70149
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Heart, Lung, and Blood Institute
- Dorothy M. Davis Heart and Lung Research Institute