Portable, Low‐Field MRI for Alzheimer’s Disease: Detecting Patterns of Atrophy Using Machine Learning Pipelines
LF-MRI acquisition at the point-of-care for MCI or AD patients is feasible, and application of ML algorithms can generate brain volumes comparable to those derived from conventional counterparts, allowing for differentiation between VC and AD/MCI subgroups. Our findings demonstrate that LF-MRI could be used as an accessible neuroimaging option for disease monitoring among those with cognitive impairment.
Authors
- Adam de Havenon (ORCID: https://orcid.org/0000-0001-8178-8597)
- Juan Eugenio Iglesias (ORCID: https://orcid.org/0000-0001-7569-173X)
- Seyedmehdi Payabvash (ORCID: https://orcid.org/0000-0003-4628-0370)
- Julia Zabinska
- John R. Dickson (ORCID: https://orcid.org/0000-0003-0135-7928)
- Teresa Gómez‐Isla (ORCID: https://orcid.org/0000-0002-7977-2075)
- John E. Kirsch (ORCID: https://orcid.org/0000-0002-0389-9475)
- Pablo Laso
- Jennifer D. Guo (ORCID: https://orcid.org/0009-0003-8438-2826)
- Ava Farnan (ORCID: https://orcid.org/0009-0002-5189-1563)
- Amanda DeSenna
- Kevin Sheth
- Liliana A Ramirez Gomez
- Annabel Sorby‐Adams
- Pamela Shaefer
- Mattew Rosen
- W. Taylor Kimberly
Institutions
- Yale New Haven Hospital (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- University College London (GB)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70862_110182
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00