Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line– area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow‑ angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the Simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2. 9 times faster. Differences in root mean square error and peak signal‑ to‑ noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0. 05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0. 05), though computations were 4. 9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM– AIM that achieves significantly improved images though with its computational requirements.

In this paper, we study Projected non-stationary Simultaneous It-erative reconstruction techniques (P-SIRT). Based on algorithmic op-erators, convergence result are adjusted with Opial’ s Theorem. The advantages of P-SIRT are demonstrated on examples taken from to-mographic imaging.

This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications that work with multiple signals maintaining some degree of dependency, e. g., radar and sensor networks. We introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion of the convergence of our method, called Simultaneous iterative Method (SIM). In this study, we compared our method with other group-sparse reconstruction techniques, namely Simultaneous Orthogonal Matching Pursuit (SOMP) and Block iterative Method with Adaptive Thresholding (BIMAT), through numerical experiments. The simulation results demonstrated that SIM outperformed these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIM is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems.

Background: The aim of this study was to evaluate the influences of iterative model reconstruction (IMR) and hybrid-iterative reconstruction (HIR) techniques on quantitative and qualitative image analysis of 256-slice coronary computed tomography angiography (CCTA). Methods: Sixty-one patients (30 men and 31 women with mean age of 60. 68 ± 9. 13 years) who had undergone CCTA by the 256-slice CT scanner were evaluated. The raw data were reconstructed using HIR and IMR algorithms. For objective assessment of image quality, parameters of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained for both reconstruction algorithms. For subjective assessment, two physician specialists evaluated image quality using a 5-point scale. Findings: The mean image noise on HIR and IMR images was 32. 74 ± 6. 04 and 25. 15 ± 3. 59, respectively. In IMR, the CNR method (28. 81 ± 5. 13) was significantly better than HIR method (23. 06 ± 5. 03) (P < 0. 001). However, the HIR method (4. 62 ± 0. 39) was higher than the IMR method (4. 48 ± 0. 45) in terms of qualitative or subjective criteria (P < 0. 001). Conclusion: The IMR method can improve quantitative criteria of image, reduce noise, and increase SNR and CNR of images better than HIR method. However, the images which reconstructed by HIR were superior to IMR in terms of qualitative criteria.

When applying the non-stationary Simultaneous iterative methods for solving an ill-posed set of linear equations, the error usually initially decreases but after some iterations, depending on the amount of noise in the data, and the degree of ill-posedness, it starts to increase. This phenomenon is called semi-convergence. We study the semi-convergence behavior of the non-stationary Simultaneous iterative methods and obtain an upper bound for data error (noise error). Based on this bound, we propose new ways to specify the relaxation parameters to control the semi-convergence. The performance of our strategies is shown by examples taken from tomographic imaging.

The problem of minimum-cost expansion of power transmission network is formulated as a genetic algorithm with the cost of new lines and security constraints and Kirchhoff’s Law at each bus bar included. A genetic algorithm (GA) is a search or optimization algorithm based on the mechanics of natural selection and genetics. An applied example is presented. The results from a set of tests carried out on the prototype show that the application of GA techniques is feasible in transmission network planning. An empirical analysis of the effects of the parameters of the algorithm is also presented in the context of this novel application. Existing mathematical programming, heuristic techniques, artificial intelligence (AI) and iterative improvement methods are also reviewed briefly.