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Title: Multi-Energy X-Ray Computed Tomography Reconstruction and Classification Algorithms for Explosives Detection

CT scans, like this one of a human's head, can be used for medical diagnosis.

CT scans, like this one of a human’s head, can be used for medical diagnosis. Karl, Ishwar, Eger, Do, and Pien are looking at how the same technology can be used in explosives detection.

Participants: Boston University – Limor Eger (PhD ’10); Professors W. Clem Karl and Prakash Ishwar

Massachusetts General Hospital – Synho Do and Homer Pien

Funding: ALERT- A Department of Homeland Security Center of Excellence

Background: X-ray computed tomography (CT) is widely used for medical diagnosis and for security purposes such as baggage inspection. CT gives a three-dimensional image of the scanned object based on its x-ray attenuation. The attenuation depends on the material being scanned and is also a function of the energy of the incident x-ray photons. In multi-energy computed tomography (MECT), multiple energy-selective measurements of the attenuation of the object are obtained. MECT measurements can be used to infer information about the chemical composition of the object, potentially allowing discrimination of materials which are indistinguishable in the conventional single-spectrum case.

Description: In this project we investigate MECT reconstruction algorithms for the purpose of material identification and explosives detection. In general, the input to such algorithms is a set of measurements collected by MECT systems and the output is a set of material-specific parameters, such as effective atomic number and density. Multi-energy equations can be easily written and solved for monochromatic energy spectra and perfect detectors but become complex when considering realistic spectra, detector sensitivity, and system non-linearity. The project aims to advance MECT detection algorithms by: (i) introducing physics-driven model-based statistical methods for extraction of information from MECT measurements, (ii) developing an understanding of the information available in MECT data that is relevant to discrimination of explosive threats, and (iii) developing and applying learning-based methods to multi-energy material discrimination.

Results: Preliminary work has been performed on extensions to current image-based multi-energy techniques, a new statistical MECT reconstruction method based on the Extended Kalman Filter and on feature-selection for discriminating between explosives and non-explosives. Initial results show promise for improved material-discrimination.

Eger-Karl-research

Above: Results of applying a dual-energy method proposed by Siemens to a scan of a suitcase with some materials provided by LLNL/DHS. The scan was performed with a Siemens SOMATOM Definition dual-source CT at Massachusetts General Hospital. On the top are two images of the same cross section obtained with different source spectra. On the bottom are the estimated effective atomic number and density images for that cross section, which are used to identify the materials.

Publications: L. Eger, S. Do, P. Ishwar, W. C. Karl, and H. Pien, “A learning-based approach to explosives detection using multi-energy X-ray computed tomography,” Submitted to ICASSP 2011.

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