SBIR Phase I: Single-shot X-ray Phase-contrast Imaging Using Deep Learning Approaches

Award ID: 2321552

The broader impact of this Small Business Innovation Research (SBIR) Phase I project relates to the benefits of next-generation X-ray imaging systems. The proposed single-shot platform overcomes the current barriers to widespread commercialization of Differential Phase Contrast (DPC) X-ray imaging. If successful, the single-shot technology will enable the development and application of next generation X-ray imagers for detecting liquids, small explosives, and other security threats for aviation and business applications. Reducing false alarm rates at airports will increase customer satisfaction, improve security, and reduce cost. DPC imaging could also substantially increase the detection of food pests, thereby reducing food waste and saving billions of dollars. In another market, non-destructive testing could significantly improve the inspection of additive manufacturing products, reducing manufacturing time through fewer iterations and creating high-quality products. Medical DPC imagers would provide MRI (Magnetic Resonance Imaging)-like resolution and diagnostics at an order of magnitude lower cost than current MRI. This Small Business Innovation Research Phase I project aims to develop a deep-learning approach to realize single-shot X-ray phase-contrast imaging. To commercialize the technology, the deep-learning algorithm needs to identify more complicated real-world objects effectively and accurately. Deep-learning methods require thousands to millions of training samples to make a reliable model. However, no imaging library for this unique technology currently exists. The research and development plan initially incorporates the standard slow scanning method of X-ray phase-contrast imaging to obtain DPC tri-signature computed tomography (CT) images. The tri-signature CT images provide the basis for precise material characterization (e.g., absorption coefficients, indices of refraction, and scatter characteristics). Once the materials have been characterized, they form the basis for creating millions of numerical representations of real-world objects. These objects subsequently form the core for effectively and efficiently training deep-learning models without further experimentation. 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 Program Director: Peter Atherton