Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-03-001 ISMRM Abstract

Clinical validation of deep learning accelerated 3D MPRAGE at 1.5T and 3T: repeatability and volumetric analyses

Primary: Neuro - Aging
Secondary: Neuro - Dementia
562-03-001 · Machine Learning in Neuroimaging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row C
Keywords: Brain Brain volume MPRAGE
Accepted
Atefeh Zeinoddini1, Eugene Milshteyn 2, MARY THOMAS2, Dan W Rettmann2, Susie Huang3,4,5, Ivan Jambor6,7
1Department of Radiology, Massachusetts General Hospital, Boston, United States of America
2GE HealthCare, San Ramon, United States of America
3Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
4Department of Radiology, Harvard Medical School, Boston, United States of America
5Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
6Radiology Enterprise Service Group, Mass General Brigham, Somerville, United States of America
7Department of Radiology, University of Turku, Raisio, Finland
Presenting Author: Eugene Milshteyn

Synopsis

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References

1. [1] R. M. Lebel, “Performance characterization of a novel deep learning-based MR image reconstruction pipeline,” Aug. 14, 2020, arXiv: arXiv:2008.06559. doi: 10.48550/arXiv.2008.06559. [doi]
2. [2] Ahn S, et al., “Deep learning-based reconstruction of highly accelerated 3D MRI,” Mar. 09, 2022, arXiv: arXiv:2203.04674. doi: 10.48550/arXiv.2203.04674. [doi]
3. [3] Ahn S, et al.: Task-based evaluation of deep learning-based reconstruction for highly-accelerated 3D T1-weighted brain MRI scans. ISMRM 2023.
4. [4] Reuter M, Schmansky NJ, Rosas HD, et al. Within-subject template estimation for unbiased robust and sensitive longitudinal image analysis. Neuroimage. 2012;61:1402-1418. PMID: 22430496 [pmid]
5. [5] Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent robust registration: a robust approach. Neuroimage. 2010;53:1181-1196. PMID: 22430496 [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-03-001