scholarly journals Minimization of Dose Load in Algorithms of X-Ray Computed Tomography

2019 ◽  
Vol 64 (4) ◽  
pp. 282
Author(s):  
L. A. Bulavin ◽  
Yu. F. Zabashta ◽  
O. V. Motolyha

An algorithm has been developed for the reconstruction of an X-ray image obtained at the minimum dose load on the researched object and provided a given image accuracy. This algorithm combines approaches typical of the inverse projection and regularization methods. The image is formed by overlaying filtered projections, and the filtering parameters are determined from the minimum condition for the difference between the discrepancy and the experimental error.

2017 ◽  
Vol 870 ◽  
pp. 223-227
Author(s):  
Hiroyuki Fujimoto ◽  
Makoto Abe ◽  
Kazuya Matsuzaki ◽  
Osamu Sato ◽  
Toshiyuki Takatsuji

The measurement capability of the dimensional X-ray Computed Tomography (DXCT) is studied by a computer simulation and the result was compared to the observed data obtained by the measurement of calibrated gauges of same shape that consist of simple geometric forms. The simulation showed that the measurement using polychromatic x-ray is different from that of monochromatic x-ray, but the difference was far smaller than expected from the results of observed data. These results indicated that the deviation of the measured values of geometric objects was not caused by simple origin and more causes have to be taken into consideration for the actual apparatus.


1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.


2021 ◽  
Author(s):  
Katherine A. Wolcott ◽  
Guillaume Chomicki ◽  
Yannick M. Staedler ◽  
Krystyna Wasylikowa ◽  
Mark Nesbitt ◽  
...  

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