Technology
Magnetic Resonance Imaging (MRI) is one of the most important diagnostic tools in medicine, providing unparalleled visualization of internal anatomy and soft tissues without harmful radiation. Despite its strengths, MRI suffers from long acquisition times, which increase patient discomfort, limit throughput, and often introduce motion artifacts that compromise diagnostic accuracy. These challenges are even more pronounced in low-field MRI systems, which offer greater accessibility and lower cost but traditionally face limitations in speed, resolution, and image quality.
Our solution applies deep learning–based reconstruction methods to overcome these barriers. By generating high-quality images from under-sampled or noisy low-field data, scan times are dramatically reduced without sacrificing diagnostic precision. Integrating physics-informed MRI models with advanced neural networks—including CNNs, unrolled architectures, generative models, and self-supervised learning—this technology enhances image clarity, suppresses artifacts, and adapts across scanners and patient types. It enables smarter acquisition strategies and makes low-field MRI faster, more robust, and more scalable, paving the way for wider clinical use and global accessibility.
Advantages
- Faster, more comfortable scans
- Improved image quality and reduced artifacts
- Robust across scanners and patient types
- Expands the utility of affordable low-field MRI systems
Applications and Opportunities
- Clinical care: reliable, cost-effective imaging in cardiology, neurology, oncology, and pediatrics.
- Research: quantitative imaging and large-scale studies in diverse settings.
- Industry & access: partnerships to deploy low-field MRI in community hospitals, outpatient clinics, and emerging markets.
Business Development Contacts
