Relaxation Factor Optimization for Common Iterative Algorithms in Optical Computed Tomography

Abstract

Optical computed tomography technique has been widely used in pathological diagnosis and clinical medicine. For most of optical computed tomography algorithms, the relaxation factor plays a very important role in the quality of the reconstruction image. In this paper, the optimal relaxation factors of the ART, MART, and SART algorithms for bimodal asymmetrical and three-peak asymmetrical tested images are analyzed and discussed. Furthermore, the reconstructions with Gaussian noise are also considered to evaluate the antinoise ability of the above three algorithms. The numerical simulation results show that the reconstruction errors and the optimal relaxation factors are greatly influenced by the Gaussian noise. This research will provide a good theoretical foundation and reference value for pathological diagnosis, especially for ophthalmic, dental, breast, cardiovascular, and gastrointestinal diseases.

Tomographic Image Reconstruction Based on Minimization of Symmetrized Kullback-Leibler Divergence

“…This was due to characteristics of the ML-EM and MART algorithms being disadvantageous. As a result of these characteristics, ML-EM has a slow convergence [28][29][30][31], which means that a relatively large number of iterations is required to obtain an acceptable value of the objective function, and MART is more susceptible to noise [32,33], which may result in an increase of the convergence rate with increasing iterative steps due to the noisy projection, as typically seen at = 0 and 1 in Figure 3, respectively.…”

Optimal linear filtering for weak target detection based on dyadic contrast function analysis in RFT

“…3. Eigenvalue and eigenvector of this cell are used to estimate the magnitude and phase of the new source in the scene [21, 27–32] In (21), are the eigenvectors and eigenvalues for each cell of the vector image defined by (18). , is the pixel vector representation.…”

“…They also focus on other parameters, such as operating frequency, number of TXs and RXs and their geometry. Improving the capability of the RFT systems to detect and reconstruct the image of weak returns that are surrounded by strong scatterers is established by the authors in [19–21]. They have considered ways to improve laser object detection when surrounded by strong scatterers in measurement domain.…”