Portable MRI Shows Promise for Expanding Brain Imaging for Alzheimer’s Disease

By leveraging machine learning tools, Mass General Brigham researchers measured markers of Alzheimer’s disease on portable MRI with accuracy matching that of standard MRI

Globally, approximately 139 million people are expected to have Alzheimer’s disease (AD) by 2050. Magnetic resonance imaging (MRI) is an important tool for identifying changes in brain structure that precede cognitive decline and progression with disease; however, its cost limits widespread use. A new study by investigators from Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system, demonstrates that a simplified, low magnetic field (LF) MRI machine, augmented with machine learning tools, matches conventional MRI in measuring brain characteristics relevant to AD. Findings, published in Nature Communications, highlight the potential of the LF-MRI to help evaluate those with cognitive symptoms.

“To tackle the growing, global health challenge of dementia and cognitive impairment in the aging population, we’re going to need simple, bedside tools that can help determine patients’ underlying causes of cognitive impairment and inform treatment,” said senior author W. Taylor Kimberly, MD, PhD, chief of the Division of Neurocritical Care in the Department of Neurology at MGH. “To do this, we brought a research team together to design a patient-centered approach to brain imaging to improve access and convenience, reduce costs, and streamline cognitive evaluations.”

The research team behind the project included clinical researchers, MRI physicists, health system delivery experts, and AI experts. Investigators at MGH have explored LF-MRI for several years as an alternative to traditional high-field (HF) MRI, which provides detail-rich, cross-sectional images of the body by generating powerful magnetic fields. HF-MRI machines are expensive, require designated imaging centers/suites for operation, and are frequently absent from low-resource settings in the U.S. and around the world. In contrast, LF-MRI machines use magnetic fields 50 times weaker than those required for conventional MRI. This makes LF-MRI scanners smaller and portable, requiring just one electrical outlet for operation, but also results in reduced image quality.

To improve LF-MRI image quality and make it easier to use in the clinic, the researchers leveraged artificial intelligence (AI) tools. The researchers created artificial datasets and matched HF- and LF-MRI scans from both healthy people and patients with neurological conditions. They used these datasets to train an algorithm to recognize features relevant to AD on LF-MRI, including the size of brain structures like the hippocampus (the brain’s memory center), and white matter hyperintensity (WMH) regions, which can indicate neurodegeneration or blood vessel problems. When they tested their method in 54 patients with mild cognitive impairment or AD-related dementia, the AI-based LF-MRI scans closely matched traditional, HF-MRI measurements of the hippocampus and white matter volume.

The new approach will require regulatory clearance and new clinical protocols, but it holds the promise of expanding neuroimaging in settings with limited MRI access. Beyond advancing AD diagnosis, LF-MRI may help streamline care for AD patients who require MRI monitoring during treatment with novel AD drugs. The portable LF-MRI could also be used in emergency rooms, community health centers, or ambulatory units, especially for patients who have experienced or are at risk of stroke.

“Access to traditional MRI is not evenly distributed and not available to everyone,” Kimberly said. “We envision a future where a person with cognitive complaints visiting a primary care physician, geriatrician or neurologist can get a brain scan, a blood test and a cognitive test, all in a single visit. Low-cost, easier to deploy technology can help provide information to clinicians, right at the bedside.”

Authorship: In addition to Kimberly, Mass General Brigham authors include Annabel J. Sorby-Adams, Jennifer Guo, Pablo Laso, John E. Kirsch, Ana-Lucia Garcia Guarniz, Pamela W. Schaefer, Matthew S. Rosen, Teresa Gomez-Isla, and J. Eugenio Iglesias. Additional authors include Julia Zabinska, Seyedmehdi Payabvash, Adam de Havenon, and Kevin N. Sheth. 

Disclosures: This study received research support from Hyperfine, Inc. (Kimberly and Sheth). Rosen is a founder and equity holder of Hyperfine, Inc. Hyperfine had no role in the conceptualization, design, analysis, preparation of the manuscript, or decision to publish. Rosen has a financial interest in DeepSpin GmbH. 

Funding: National Institutes of Health (EB031114), the Alzheimer’s Association (24AARG-NTF-1187394), the Fulbright Commission, and the American Heart Association-Tedy’s Team postdoctoral fellowship award, NIH BRAIN Initiative (RF1 MH123195, UM1 MH130981), NIH grants (R01 AG07098 and RF1 AG080371). Rosen acknowledges the gracious support of the Kiyomi and Ed Baird MGH Research Scholar Award. 

Paper cited: Sorby-Adams, A.J., Guo, J., Laso, P. et al. “Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease.” Nat Communications DOI:10.1038/s41467-024-54972-x

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