NEURALTRAK, INC

NSF Awards · FY 2026 · 2026-06

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to expand access to advanced three-dimensional X-ray imaging during surgery without requiring hospitals to purchase expensive computed tomography scanners. Many surgical procedures, particularly spine operations, rely on two-dimensional X-ray images that can make it difficult to fully visualize anatomy and implanted hardware. Limited access to affordable three-dimensional imaging can increase procedure time, complication rates, and overall healthcare costs. This project seeks to enable existing mobile X-ray systems to produce high-quality three-dimensional images, allowing more procedures to be safely performed in outpatient surgical centers and community hospitals. If successful, the technology could reduce healthcare expenditures, improve patient safety, and lower radiation exposure by avoiding repeat scans. Commercially, the approach supports a scalable software-based model that upgrades widely deployed imaging equipment rather than replacing it, creating a large potential market across surgical centers in the United States and globally. Broader societal benefits include improved access to high-quality surgical care in rural and underserved regions, workforce development in advanced manufacturing and artificial intelligence, and strengthened national leadership in medical imaging innovation. This Small Business Innovation Research (SBIR) Phase II project aims to develop and clinically validate a new method for generating three-dimensional images from limited-angle X-ray data acquired by standard mobile surgical imaging systems. Conventional mobile systems primarily produce flat, two-dimensional images because they rotate over a small angle and operate under radiation dose constraints, limiting their ability to create accurate three-dimensional reconstructions. The project will refine artificial intelligence models that incorporate physical principles of X-ray imaging to reconstruct volumetric images from limited data. Research objectives include improving image quality and reliability across different imaging systems and patient anatomies, developing real-time calibration methods to correct for mechanical motion and geometric distortion, and validating performance in realistic surgical environments. The anticipated technical results are rapid, low-dose three-dimensional reconstructions with image clarity and geometric accuracy comparable to conventional computed tomography for specific surgical tasks. Successful completion of this work would demonstrate a practical pathway to deliver advanced three-dimensional guidance and navigation using equipment that is already widely available in operating rooms. 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.

NSF Awards · FY 2024 · 2024-09

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable local (and global) access to high-quality, low-cost, and low-radiation exposure three-dimensional (3D) computed tomography (CT) imaging using existing 2D equipment. Examples include walk-in clinics, lung cancer screening centers, rapid stroke assessment centers, mobile platforms (e.g., ambulances), battlefield hospitals, and clinics in rural and underserved areas. This advance will result in greater public access to advanced healthcare and should result in substantially lower healthcare costs. For example, moving complex spine surgeries from hospitals to local ambulatory surgical centers (ASCs) can save payors $10B in costs annually. The ASCs will also benefit; a relatively low percentage of complex monthly procedures can double their profits. Patient satisfaction should improve by moving more complicated spine surgical procedures to smaller ASCs closer to home with fewer infection risks. The useful life of legacy X-ray systems will be extended following conversion to 3D, thereby reducing waste and landfill space. Beyond medicine, the project technology has widespread applications in nondestructive testing, from manufacturing to failure analysis/prevention to archaeology and art! All these advantages will enhance US competitiveness. This Small Business Innovation Research (SBIR) Phase I project will enable simple, small-footprint, mobile, two-dimensional (2D) X-ray imaging systems to generate three-dimensional (3D) computed tomography (CT) images at low cost, with one-third of the X-ray dose of a conventional CT scan. The project combines recent advances in imaging physics with artificial intelligence (AI) to overcome the limitations of current CT image acquisition. This contrasts with conventional AI-based CT de-noising (image cleaning) algorithms that function only in the image domain with no physics input. The project has three primary research objectives. First, enhance deep learning-based image reconstruction's ability to produce high-quality images from limited data. Second, devise real-time geometric calibration methods to overcome mechanical instabilities inherent to simple X-ray systems. Third, develop high speed and high image fidelity data transfer methods to interface existing hospital imaging systems to the project computing platform while maintaining FDA and HIPPA compliance and avoiding disruption of hospital workflow. Successful development of the three core technologies described will be used to create a minimum viable product (MVP). Medical practitioners will use the MVP to evaluate the technology and refine the features needed for a clinical product. 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.