F3-A5: Model-Based Iterative Reconstruction for Advanced Baggage Screening

While traditional direct reconstruction algorithms such as filtered back projection (FBP) and direct Fourier method (DFM) depend on the analytic inversion of sinogram data, model-based reconstruction methods rely on iterative optimization of a statistical model of both the acquired data and the unknown image. Model-based iterative reconstruction (MBIR) potentially offers many important advantages over traditional methods of direct reconstruction for the security screening of checked baggage. It has the potential to reduce metal artifacts, improve resolution, reduce artifacts such as cupping that can systematically distort CT number estimates, incorporate tighter integration of reconstruction and segmentation, and support reconstruction of scanners with a small number of projections. All these improvements have the potential to improve the detection/false alarm tradeoff for CT security screening systems. The objective of this research is to implement an MBIR algorithm on a widely deployed multislice helical CT security scanner, and assess qualitatively and quantitatively the improvements over direct reconstruction. The MBIR implementation entails the accurate modeling of both the system geometry, including subtle manufacturing deviations, as well as the photon and electronic noise characteristics. The quality assessment was carried out using a set of 12 vendor-provided bag scans, selected with the guidance of the ALERT Center. The results demonstrate significant quality improvements over the native DFM reconstructions, including improved metal artifact suppression, spatial resolution, and CT value uniformity.