Portable MRI developer Hyperfine has secured a $3.3 million (2.8 million euro) grant from the Bill & Melinda Gates Foundation to expand a current research initiative exploring the utility of its portable MRI technology in developing countries.
The grant will be used to broaden an ongoing global research program to assess the clinical feasibility of the vendor's Swoop portable MRI system in providing immediate point-of-care brain imaging to young children between the ages of 0-24 months in low-and middle-income countries, Hyperfine said. The research program was initially funded in November 2020 by a $1.6 million (1.4 million euro) grant from the Bill & Melinda Gates Foundation to investigate if Hyperfine's portable MRI technology could identify and potentially mitigate labor- and delivery-related brain damage resulting in hypoxic-ischemic encephalopathy in infants.
As part of the program's expansion, Hyperfine said it's now deploying 25 Swoop systems across multiple research and clinical study sites in Europe, Asia, and sub-Saharan Africa. This effort will aim to better understand the value of low-field MRI in delivering accessible neuroimages that could enable early therapeutic interventions -- and potentially become the standard of care -- for infants and children during critical developmental stages, according to the company.
In addition, Hyperfine will also establish and coordinate a point-of-care MRI consortium that will optimize Swoop's image acquisition protocols and data quality for neonatal and infant brain imaging. Data will be generated and shared from a range of settings.
MRI research centers will develop and optimize sequences, while neonatal and pediatric brain imaging clinical centers will evaluate the system's data quality and information content relative to the reference standard of high-field MRI, according to the company.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
