| Low-dose X-ray computed tomography (CT) imaging is desirable due to the growing concerns about excessive radiation exposure to the patients. However, the reconstructed CT images by the conventional filtered back-projection (FBP) method from the low-dose acquisitions may be severely degraded. Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose CT as compared to the FBP method. According to the maximum a posteriori (MAP) estimation, the SIR methods can be typically formulated by an objective function consisting of two terms: (1) data-fidelity term modeling the statistics of projection measurements, and (2) regularization term reflecting prior knowledge or expectation on the characteristics of the image to be reconstructed. Statistical modeling of the projection measurements is a prerequisite for SIR, while the regularization term in the objective function also plays a critical role for successful image reconstruction. The objective of this dissertation is investigating accurate statistical models and novel regularization strategies for SIR to improve CT image quality in low-dose cases. Specifically, we proposed two texture-preserving regularizations based on the Markov random field (MRF) model and one generic regularization based on the nonlocal means (NLM) filter. The feasibility and efficacy of the proposed strategies are explicitly explored in this dissertation, using both computer simulation and real data (i.e., physical phantoms and clinical patients). |
