A Model-Based 3D Multi-slice Helical CT Reconstruction Algorithm for Transportation Security Application

Computed tomography (CT) is widely used in transportation security applications [1], [2]. Among various CT scanner geometries, multi-slice helical CT has come into wide use due to its fast acquisition and large scan coverage. In fact, many airports have installed multi-slice helical CT systems as a central component of baggage screening. However, the more complex geometry of multi-slice helical CT also poses challenges in reconstruction. For example, as cone angles become wider, there is an increasing need to use true 3D reconstruction methods in order to avoid the image artifacts introduced by 2D approximations. Moreover, the task in transportation security is quite different from the medical problem. In the medical application, it is critical to preserve fine details of soft tissue structure; however, in the security application, typically it is more important to obtain precise estimates of object boundary and its average density. Also, in security, the objects typically have densities that are substantially greater than water. Recently, model-based reconstruction (MBR) algorithms have been shown to be effective in the reconstruction of multislice helical scan CT data [3]. These algorithms have the advantage that they can incorporate more detailed models of both the scanner and the objects being reconstructed. In addition, they offer flexibility in the application of transportation security since they allow for more accurate reconstruction for nontraditional geometries, such as with limited view data [4]. Model-based algorithms have the potential to more accurately account for a wide array of scanner characteristics including