<scp>DeepEMC</scp>‐<scp>T<sub>2</sub></scp> mapping: Deep learning–enabled <scp>T<sub>2</sub></scp> mapping based on echo modulation curve modeling
DeepEMC-T<sub>2</sub> mapping enables simplified, efficient, and accurate T<sub>2</sub> quantification directly from MESE images without dictionary matching. Accurate T<sub>2</sub> estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.
Authors
- Daniel K. Sodickson (ORCID: https://orcid.org/0000-0002-2436-4664)
- Fang Liu (ORCID: https://orcid.org/0000-0001-8032-6681)
- Haoyang Pei (ORCID: https://orcid.org/0009-0007-9258-673X)
- Noam Ben‐Eliezer (ORCID: https://orcid.org/0000-0003-2944-6412)
- Timothy M. Shepherd (ORCID: https://orcid.org/0000-0003-0232-1636)
- Yao Wang (ORCID: https://orcid.org/0000-0003-3199-3802)
- Li Feng (ORCID: https://orcid.org/0000-0002-8692-7645)
Institutions
- Harvard University (US)
- Tel Aviv University (IL)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- New York University (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2024-08-11
- DOI
- https://doi.org/10.1002/mrm.30239
- Citations
- 4
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Center for Advanced Imaging Innovation and Research
- Centre d'Imagerie BioMédicale
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering