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Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms. [ABSTRACT FROM AUTHOR]

In the X-ray CT research area, the linear integral projections of mathematical phantoms were extensively used to evaluate the proposed algorithms with a hypothesis of monochromatic model. However, X-ray tube emits a spectrum of energies and hence it becomes difficult to study some important aspects, such as the reconstructed density accuracy of special reconstruction algorithm and beam hardening effects of polychromatic imaging. If the energy spectrum of X-ray tube, geometry of phantom, and material compositions of all sub-regions are known, the actual projections can be simulated with more accuracy. Motivated by this fact, we propose an improved three-step discrete scheme to simulate the X-ray projections based on physical model. First, CT-numbers of all sub-regions of a phantom are determined in advance. Next, CT-numbers are decomposed into different material compositions under the constraint that sub-regions correspond to different human tissues. Finally, the projections are measured according to the energy spectrum distribution of X-ray tube, using the knowledge of X-ray imaging physics and our strategy to simulate the projecting process. Our scheme requires significantly less storage and computation to achieve a higher accuracy and precision. To demonstrate the feasibility of our scheme, we use the well-known FORBILD head phantom to generate projections and the Feldkamp algorithm for image reconstruction. The anticipated beam hardening effects are clearly seen in images reconstructed from projections simulated using our scheme. This scheme can be utilized in many medical X-ray imaging research aspects, such as algorithm design, performance analysis, distortion calibrations, artifact reduction, etc. [ABSTRACT FROM AUTHOR]