Purpose: Inverting the discrete x-ray transform (DXT) with the nonlinear partial volume (NLPV) effect, which we refer to as the NLPV DXT, remains of theoretical and practical interest. We propose an optimization-based algorithm for accurately and directly inverting the NLPV DXT.
Methods: Formulating the inversion of the NLPV DXT as a nonconvex optimization program, we propose an iterative algorithm, referred to as the nonconvex primal-dual (NCPD) algorithm, to solve the problem. We obtain the NCPD algorithm by modifying a first-order primal-dual algorithm to address the nonconvex optimization. Subsequently, we perform quantitative studies to verify and characterize the NCPD algorithm.
Results: In addition to proposing the NCPD algorithm, we perform numerical studies to verify that the NCPD algorithm can reach the devised numerically necessary convergence conditions and, under the study conditions considered, invert the NLPV DXT by yielding numerically accurate image reconstruction.
Conclusion: We have developed and verified with numerical studies the NCPD algorithm for accurate inversion of the NLPV DXT. The study and results may yield insights into the effective compensation for the NLPV artifacts in CT imaging and into the algorithm development for nonconvex optimization programs in CT and other tomographic imaging technologies.
In a standard data model for CT, a single ray often is assumed between a detector bin and the X-ray focal spot even though they are of finite sizes. However, due to their finite sizes, each pair of detector bin and X-ray focal spot necessarily involves multiple rays, thus resulting in the non-linear partial volume (NLPV) effect. When an algorithm developed for a standard data model is applied to data with NLPV effect, it may engender NLPV artifacts in images reconstructed. In the presence of the NLPV effect, data necessarily relates non-linearly to the image of interest, and image reconstruction free of NLPV is thus tantamount to inverting appropriately the non-linear data model. In this work, we develop an optimization-based algorithm for solving the non-linear data model in which the NLPV effect is included, and use the algorithm to investigate the characteristics and reduction of the NLPV artifacts in images reconstructed. The algorithm, motivated by our previous experience in dealing with a non-linear data model in multispectral CT reconstruction, compensates for the NLPV effect by numerically inverting the non-linear data model through solving a non-convex optimization program. The algorithm, referred to as the non-convex Chambolle-Pock (ncCP) algorithm, is used in simulation studies for numerically characterizing the inversion of the non-linear data model and the compensation for the NLPV effect.
In this work, we investigate the non-linear partial volume (NLPV) effect caused by sub-detector sampling in CT. A non-linear log-sum of exponential data model is employed to describe the NLPV effect. Leveraging our previous work on multispectral CT reconstruction dealing with a similar non-linear data model, we propose an optimization-based reconstruction method for correcting the NLPV artifacts by numerically inverting the non-linear model through solving a non-convex optimization program. A non-convex Chambolle-Pock (ncCP) algorithm is developed and tailored to the non-linear data model. Simulation studies are carried out with both discrete and continuous FORBILD head phantom with one high-contrast ear section on the right side, based on a circular 2D fan-beam geometry. The results suggest that, under the data condition in this work, the proposed method can effectively reduce or eliminate the NLPV artifacts caused by the sub-detector ray integration.
The detection of road rutting is of great significance for reducing traffic accidents, verifying the degree of road damage, and improving the comfort of driving. The fast, real-time and accurate stripe center extraction algorithm is the key to ensure the real-time and stable operation of the system, which will directly affect the ultimate rut depth acquisition accuracy. For the existing laser stripe center extraction methods cannot meet the characteristics of good robustness, high precision, real-time, strong anti-noise ability at the same time. A signal correlation method, which always used in radar data processing, is proposed in this paper to extract the center of the laser stripes. The above algorithm can solve the problem of disconnection, but due to the use of a fixed reference column, the selection of the reference column is accidental, the relevant results will be affected by different reference columns. In order to solve this problem, this paper uses the method of multi-column data correlation to take the maximum value. The algorithm is simple and practical, which can achieve the sub-pixel level extraction accuracy, meet the real-time requirements, has strong anti-noise ability and repair ability for broken lines. The experimental results show that the algorithm is insensitive to high light intensity background, and the laser stripes center extraction accuracy can reach 0.15mm, which is far less than the 1mm inspection requirement for rutting road test specifications.
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