Reduction of metal artifact in single photon-counting computed tomography by spectral-driven iterative reconstruction technique.

PURPOSE: The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR). METHOD: The proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods. RESULTS: Decomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms. CONCLUSION: It is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers.

Gene therapy with recombinant adenovirus encoding endostatin encapsulated in cationic liposome in coxsackievirus and adenovirus receptor-deficient colon carcinoma murine models.

Adenovirus (Ad)-based antiangiogenesis gene therapy is a promising approach for cancer treatment. Downregulation or loss of coxsackievirus and adenovirus receptor (CAR) is often detected in various human cancers, which hampers adenoviral gene therapy approaches. Cationic liposome-complexed adenoviral vectors have been proven useful in CAR-deficient cells to enhance therapeutic gene transfer in vivo. Here, we investigated the antitumor effects of recombinant adenovirus encoding endostatin (Ad-hE) encapsulated in cationic liposome (Ad-hE/Lipo) on CAR-deficient CT26 colon carcinoma murine models. In vitro, Ad-hE/Lipo enhanced adenovirus transfection in CAR-deficient cells (CT26), and endostatin gene expression was measured by both qualitative and quantitative detection. In addition, an antibody neutralizing assay indicated that neutralizing serum inhibited naked adenovirus 5 (Ad5) at rather higher dilution than the complexes of Ad5 and cationic liposomes (Ad5-CL), which demonstrated that Ad5-CL was more capable of protecting Ad5 from neutralization. In vivo, Ad-hE/Lipo treatment in the murine CT26 tumor model by intratumoral injection resulted in marked suppression of tumor growth and prolonged survival time, which was associated with a decreased number of microvessels and increased apoptosis of tumor cells. In conclusion, recombinant endostatin adenovirus encapsulated with cationic liposome effectively inhibited CAR-deficient tumor growth through an antiangiogenic mechanism in murine models without marked toxicity, thus showing a feasible strategy for clinical applications.