Name
Abstract | ||
|---|---|---|
Computed Tomography (CT) is widely used for transportation security to screen baggage for potential threats. For example, many airports use X-ray CT to scan the checked baggage of airline passengers. The resulting reconstructions are then used for both automated and human detection of threats. Recently, there has been growing interest in the use of model-based reconstruction techniques for application in CT security systems. Model-based reconstruction offers a number of potential advantages over more traditional direct reconstruction such as filtered backprojection (FBP). Perhaps one of the greatest advantages is the potential to reduce reconstruction artifacts when non-traditional scan geometries are used. For example, FBP tends to produce very severe streaking artifacts when applied to limited view data, which can adversely affect subsequent processing such as segmentation and detection.In this paper, we investigate the use of model-based reconstruction in conjunction with limited-view scanning architectures, and we illustrate the value of these methods using transportation security examples. The advantage of limited view architectures is that it has the potential to reduce the cost and complexity of a scanning system, but its disadvantage is that limited-view data can result in structured artifacts in reconstructed images. Our method of reconstruction depends on the formulation of both a forward projection model for the system, and a prior model that accounts for the contents and densities of typical baggage. In order to evaluate our new method, we use realistic models of baggage with randomly inserted simple simulated objects. Using this approach, we show that model-based reconstruction can substantially reduce artifacts and improve important metrics of image quality such as the accuracy of the estimated CT numbers. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.917781 | COMPUTATIONAL IMAGING X |
Keywords | Field | DocType |
computed tomography, iterative reconstruction, Markov random field, limited view, transportation security | Iterative reconstruction,Computer vision,Segmentation,Markov random field,Image quality,Artificial intelligence,Computed tomography,Hounsfield scale,Streaking,Physics | Conference |
Volume | ISSN | Citations |
8296 | 0277-786X | 3 |
PageRank | References | Authors |
0.44 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sherman J. Kisner | 1 | 14 | 1.71 |
| Eri Haneda | 2 | 12 | 1.33 |
| Charles A. Bouman | 3 | 2740 | 473.62 |
| Sondre Skatter | 4 | 3 | 0.44 |
| Mikhail Kourinny | 5 | 3 | 0.44 |
| Simon Bedford | 6 | 3 | 0.44 |