Multi-modal Imaging for Portal-based Screening Multispectral Methods for Diffraction Tomography
This project investigates the development of automated explosives detection and classification algorithms for increased throughput by using combinations of sensors in an active, adaptive testing scheme. Multi-modal sensors can help find and distinguish the features of existing threats, and even discover and classify new ones. The significance of this project lies in the potential to use multiple modalities fused together to detect the presence of explosives, for both portal and stand-off systems, and then to classify their natures as specifically and sensitively as possible. In our recent work, we have developed new theories for increasing the signal/noise ratio in diffraction tomography using sensors that collect measurements at multiple frequencies, by adapting techniques previously exploited for multi-modal imaging in medical applications. This past year, we have extended this work to include fusion of X-ray diffraction tomography along with conventional X-ray computed tomography, in order to extract information from coherent scatter of materials to fuse with the conventional CT absorption images.
My involvement with ALERT has modified my research agenda to integrate far greater knowledge of security threats and sensing modalities that are capable of extracting relevant information for detection and classification of such threats.- Project Leader, David Castanon
Faculty and Staff Currently Involved in Project
W. Clem Karl
Students Currently Involved in Project
- Ke Chen