Material classification using convolution neural network (CNN) for X-ray based coded aperture diffraction system

Transmission x-ray systems rely on the measured photon attenuation coefficients for material imaging and classification. While this approach provides high quality imaging capabilities and satisfactory object discrimination in most situations, it lacks material-specific information. For airport security, this can be a significant issue as false alarms require additional time to be resolved by human operators, which impacts bag throughput and airport operations. Orthogonal techniques such as X-ray Diffraction Tomography (XRDT) using a coded aperture provide complementary chemical/molecular signatures that can be used to identify a target material. The combination of noisy signals, variability in the XRD form factors for the same material, and the lack of a comprehensive material library limits the classification performance of the correlation based methods. Using simulated data to train a 1D Convolution Neural Network (CNN), we found relative improvements in classification accuracy compared to the correlation based approach we used previously. These improvement gains were cross-validated using the simulated data, and provided satisfactory detection results against real experimental data collected on a laboratory prototype.