Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease

Annabel J. Sorby-Adams, Jennifer Guo, Pablo Laso, John E. Kirsch, Julia Zabinska, Ana-Lucia Garcia Guarniz, Pamela W. Schaefer, Seyedmehdi Payabvash, Adam Havenon, Matthew S. Rosen, Kevin N. Sheth, Teresa Gomez-Isla, J. Eugenio Iglesias and W. Taylor Kimberly ()
Additional contact information
Annabel J. Sorby-Adams: Massachusetts General Hospital and Harvard Medical School
Jennifer Guo: Massachusetts General Hospital and Harvard Medical School
Pablo Laso: Massachusetts General Hospital and Harvard Medical School
John E. Kirsch: Massachusetts General Hospital and Harvard Medical School
Julia Zabinska: Yale New Haven Hospital and Yale School of Medicine
Ana-Lucia Garcia Guarniz: Massachusetts General Hospital and Harvard Medical School
Pamela W. Schaefer: Massachusetts General Hospital and Harvard Medical School
Seyedmehdi Payabvash: Yale New Haven Hospital and Yale University School of Medicine
Adam Havenon: Yale New Haven Hospital and Yale School of Medicine
Matthew S. Rosen: Massachusetts General Hospital and Harvard Medical School
Kevin N. Sheth: Yale New Haven Hospital and Yale School of Medicine
Teresa Gomez-Isla: Massachusetts General Hospital and Harvard Medical School
J. Eugenio Iglesias: Massachusetts General Hospital and Harvard Medical School
W. Taylor Kimberly: Massachusetts General Hospital and Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer’s disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-noise ratio. Here, we optimize LF-MRI acquisition and develop a freely available machine learning pipeline to quantify brain morphometry and white matter hyperintensities (WMH). We validate the pipeline and apply it to outpatients presenting with mild cognitive impairment or dementia due to AD. We find hippocampal volumes from ≤ 3 mm isotropic LF-MRI scans have agreement with conventional MRI and are more accurate than anisotropic counterparts. We also show WMH volume has agreement between manual segmentation and the automated pipeline. The increased availability and reduced cost of LF-MRI, in combination with our machine learning pipeline, has the potential to increase access to neuroimaging for dementia.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54972-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54972-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54972-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().