Equipment: MRI (Track 1): Acquisition of Ultra-low Field Portable Magnetic Resonance Imaging Scanner (Hyperfine Swoop)
- Institution
- University of Connecticut, CT
- Principal investigator
- Nabin Koirala
- Funding
- $603K
- Duration
- 2026–2029
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Still being scored
Abstract
Brain imaging is a powerful tool scientists have for understanding how people learn, communicate, and recover from illness or injury, yet until recently, it has only been available in specialized hospitals and university labs, leaving out many people who cannot easily travel or who are not comfortable in a large, noisy machine. This award supports the acquisition of the Hyperfine Swoop, an FDA-cleared portable Magnetic Resonance Imaging (MRI) scanner that plugs into a standard wall outlet, requires no special shielding room, and can be transported in a mobile research laboratory to schools, community centers, clinics, and regional university campuses. By bringing brain imaging directly to participants this instrument will make neuroscience more accessible and collect data that is more representative of all Americans. Additionally, the instrument will support the research training of graduate students, undergraduates, and the next generation of scientists across institutions and geographic communities. The instrument is housed at a research university and transported via a mobile research laboratory to regional campuses, partner institutions, schools, and community sites for field-based data collection. A multidisciplinary team of faculty members spanning neuroscience, speech and language sciences, kinesiology, psychology, and psychiatry plan to use the instrument to address research questions related to cognition, behavior, and health. By expanding data collection to a wider away of Americans and enabling imaging of populations with contraindications for high-field conventional MRI, this instrument advances precision neuroscience and broadens the evidence base that informs research and health outcomes. The research supported uses advanced machine learning and artificial intelligence techniques for data analyses for projects that have impacts for education and health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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