NeuralTrak provides a physics‑informed generative AI software layer that upgrades existing medical imaging equipment to deliver real‑time, low‑dose 3D visualizations. By reconstructing high‑fidelity volumetric images from standard 2D scans, it reduces radiation exposure and hardware costs, enabling hospitals and surgical centers to improve image guidance and procedural accuracy.
Funding
Funding not disclosed
Founders
Product
Problem
Conventional medical imaging systems often require high radiation doses and expensive hardware to generate three-dimensional images, limiting their accessibility and increasing patient risk during surgical and diagnostic procedures.
Solution
NeuralTrak applies physics-informed generative AI to augment existing imaging equipment, enabling real-time, low-dose 3D visualization without the need for costly hardware upgrades. The AI model leverages known physical imaging principles to reconstruct volumetric data from standard 2D acquisitions, preserving image quality while reducing radiation exposure. This approach allows healthcare facilities to deploy advanced image guidance solutions broadly, improving procedural accuracy and patient safety across a range of clinical settings. By delivering the technology as a software layer, NeuralTrak keeps implementation costs low and supports rapid integration with current imaging workflows.
Target Audience
Primary customers are hospitals, surgical centers, and diagnostic imaging clinics seeking to enhance image guidance capabilities while controlling costs and radiation exposure.
Features
- Physics-informed generative AI engine that reconstructs high-fidelity 3D images from low-dose 2D inputs
- Real-time processing suitable for intraoperative guidance and immediate diagnostic feedback
- Compatibility with a wide range of existing imaging hardware, eliminating the need for new equipment purchases
- Radiation dose reduction algorithms that maintain diagnostic image quality while minimizing patient exposure
- Scalable software deployment model that can be installed on local servers or cloud infrastructure