Connecticut Patent of the Month – November 2024
Revolutionizing magnetic resonance imaging (MRI) technology, Hyperfine introduces an innovative method that bridges the gap between traditional high-field and low-field MRI systems through advanced artificial intelligence (AI). By generating synthetic training data tailored to specific MRI systems, Hyperfine’s approach empowers neural networks to process data with unprecedented accuracy. This novel system utilizes reference magnetic resonance (MR) volumes to simulate the unique spatial, resolution, and interference characteristics of target MRI systems, enabling efficient and precise training of AI models.
At the core of this breakthrough is the creation of synthetic spatial frequency data enriched with detailed field characteristics and realistic artifacts. This data generation is achieved through a meticulous process involving synthetic phase integration, advanced transformations, and coil-specific simulations. These steps replicate real-world scenarios, such as patient motion or system interference, ensuring the AI models are robustly trained for diverse conditions.
Hyperfine’s methodology not only enhances the performance of low-field MRI systems but also significantly reduces the computational and financial barriers traditionally associated with high-field imaging. The integration of neural networks for image reconstruction further amplifies the potential of this technology, allowing for clearer, more accurate diagnostics even in resource-limited settings.
This invention is set to redefine accessibility in MRI diagnostics, bringing high-quality imaging to more communities worldwide. By leveraging state-of-the-art AI and a deep understanding of MR physics, Hyperfine is transforming medical imaging into a more inclusive and adaptable tool for global health.
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