Dynamic frame-by-frame motion correction for 18F-flurpiridaz PET-MPI using convolution neural network
DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for stress MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing <sup>18</sup>F-flurpiridaz PET-MPI.
Authors
- Aditya Killekar (ORCID: https://orcid.org/0000-0003-3831-5676)
- Paul Kavanagh (ORCID: https://orcid.org/0000-0002-2242-1936)
- Mark A. Lemley (ORCID: https://orcid.org/0000-0002-2944-0582)
- Piotr J. Slomka (ORCID: https://orcid.org/0000-0002-6110-938X)
- Meghana Urs
- Chih-Chun Wei
- C. J. Buckley (ORCID: https://orcid.org/0009-0005-3118-365X)
- Giselle Ramirez (ORCID: https://orcid.org/0009-0004-3730-5075)
- Valerie Builoff
Institutions
- Cedars-Sinai Medical Center (US)
- General Electric (Spain) (ES)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1007/s00259-025-07660-x
- Citations
- 1
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute