scholarly journals Sensitivity to X-Ray Computed Tomography Instrument Geometry Errors as a Function of Rotation Stage Position, Detector Position, and Detector Size

2021 ◽  
Author(s):  
Bala Muralikrishnan ◽  
Prashanth Jaganmoha ◽  
Meghan Shilling ◽  
Ed Morse
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
Detector Position ◽  
X Ray Computed ◽  
Rotation Stage

scholarly journals X-Ray Computed Tomography Instrument Performance Evaluation, Part III: Sensitivity to Detector Geometry and Rotation Stage Errors at Different Magnifications

2021 ◽  
Vol 126 ◽  
Author(s):  
Prashanth Jaganmohan ◽  
Bala Muralikrishnan ◽  
Meghan Shilling ◽  
Edward Morse
Keyword(s):  
Computed Tomography ◽  
Performance Evaluation ◽  
Standardized Test ◽  
Optimal Placement ◽  
Test Procedures ◽  
X Ray ◽  
Measurement Volume ◽  
Detector Geometry ◽  
X Ray Computed ◽  
Rotation Stage

With steadily increasing use in dimensional metrology applications, especially for delicate parts and those with complex internal features, X-ray computed tomography (XCT) has transitioned from a medical imaging tool to an inspection tool in industrial metrology. This has resulted in the demand for standardized test procedures and performance evaluation standards to enable reliable comparison of different instruments and support claims of metrological traceability. To meet these emerging needs, the American Society of Mechanical Engineers (ASME) recently released the B89.4.23 standard for performance evaluation of XCT systems. There are also ongoing efforts within the International Organization for Standardization (ISO) to develop performance evaluation documentary standards that would allow users to compare measurement performance across instruments and verify manufacturer’s performance specifications. Designing these documentary standards involves identifying test procedures that are sensitive to known error sources. This paper, which is the third in a series, focuses on geometric errors associated with the detector and rotation stage of XCT instruments. Part I recommended positions of spheres in the measurement volume such that the sphere center-to-center distance error and sphere form errors are sensitive to the detector geometry errors. Part II reported similar studies on the errors associated with the rotation stage. The studies in Parts I and II only considered one position of the rotation stage and detector; i.e., the studies were conducted for a fixed measurement volume. Here, we extend these studies to include varying positions of the detector and rotation stage to study the effect of magnification. We report on the optimal placement of the stage and detector that can bring about the highest sensitivity to each error.

scholarly journals X-ray Computed Tomography Instrument Performance Evaluation, Part II: Sensitivity to Rotation Stage Errors

2019 ◽  
Vol 124 ◽  
Author(s):  
Bala Muralikrishnan ◽  
Megan Shilling ◽  
Steve Phillips ◽  
Wei Ren ◽  
Vincent Lee ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Performance Evaluation ◽  
Test Procedures ◽  
X Ray ◽  
Measurement Volume ◽  
Form Errors ◽  
Sphere Center ◽  
X Ray Computed ◽  
Rotation Stage ◽  
Center Distance

The development of standards for evaluating the performance of X-ray computed tomography (XCT) instruments is ongoing within the American Society of Mechanical Engineers (ASME) and the International Organization for Standardization (ISO) working committees. A key challenge in developing documentary standards is to identify test procedures that are sensitive to known error sources. In Part I of this work, we described the effect of geometry errors associated with the detector and determined their influence through simulations on sphere center-to-center distance errors and sphere form errors for spheres located in the tomographically reconstructed measurement volume. We also introduced a new simulation method, the single-point ray tracing method, to efficiently perform the distance and form error computations and presented data validating the method. In this second part, also based on simulation studies, we describe the effect of errors associated with the rotation stage on sphere center-to-center distance errors and sphere form errors for spheres located in the tomographically reconstructed measurement volume. We recommend optimal sphere center locations that are most sensitive to rotation stage errors for consideration by documentary standards committees in the development of test procedures for performance evaluation.

X-Ray Computed Tomography of Ultralightweight Metals

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

scholarly journals Nano- scale X-ray Computed Tomography: Morphological Analysis of Fuel Cell Electrodes

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster
Keyword(s):  
Computed Tomography ◽  
Fuel Cell ◽  
Morphological Analysis ◽  
Fuel Cell Electrodes ◽  
X Ray ◽  
Nano Scale ◽  
X Ray Computed

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

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

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.

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