Very Low-Intensity Throughput X-Ray Computed Tomography of a Cast FeMnAl Steel Alloy

Author(s):  
William H. Green ◽  
Bryan A. Cheeseman ◽  
Daniel Field ◽  
Krista R. Limmer

Abstract The X-ray computed tomography (XCT) technique is a widely applicable and powerful non-destructive inspection modality for evaluation and analysis of geometrical and physical characteristics of materials, especially internal structures and features. XCT is applicable to metals, ceramics, plastics, and polymer and mixed composites, as well as components and materiel. The Army Research Laboratory (ARL) and its partners are currently investigating the use of cast iron-manganese-aluminum (FeMnAl) steel alloy material in support of weight reduction initiatives in Army Development Programs. Steel alloy FeMnAl has been identified as a key enabling material technology to reduce the weight in ground combat vehicle systems. A set of FeMnAl blocks each approximately 50.8 mm (2 in.) thick by 76.2 mm (3 in.) wide by 76.2 mm (3 in.) long, which had been sectioned from an industrially cast ingot (∼12,000 lbs.), were individually scanned by XCT using a conventional 450 kV X-ray source and a solid-state flat panel detector. Mainly due to the thickness of the blocks, as well as a desire to keep geometric unsharpness relatively small which affected overall scan geometry (set up), the scans had a very low response at the detector through the FeMnAl blocks. With the calibrated detector response through air (i.e., around a block) at 85–90% the response through the block was only 5–10%. The XCT scanning parameters and overall protocol used to mitigate the very low-intensity throughput and achieve acceptable scan image results will be discussed. Image processing (IP) methods used to segment porosity features in the FeMnAl blocks will also be discussed.

2020 ◽  
Vol 53 (6) ◽  
pp. 1444-1451
Author(s):  
Maik Kahnt ◽  
Simone Sala ◽  
Ulf Johansson ◽  
Alexander Björling ◽  
Zhimin Jiang ◽  
...  

Ptychographic X-ray computed tomography is a quantitative three-dimensional imaging technique offered to users of multiple synchrotron radiation sources. Its dependence on the coherent fraction of the available X-ray beam makes it perfectly suited to diffraction-limited storage rings. Although MAX IV is the first, and so far only, operating fourth-generation synchrotron light source, none of its experimental stations is currently set up to offer this technique to its users. The first ptychographic X-ray computed tomography experiment has therefore been performed on the NanoMAX beamline. From the results, information was gained about the current limitations of the experimental setup and where attention should be focused for improvement. The extracted parameters in terms of scanning speed, size of the imaged volume and achieved resolutions should provide a baseline for future users designing nano-tomography experiments on the NanoMAX beamline.


Author(s):  
Gengsheng L. Zeng ◽  
Megan Zeng

AbstractWhen the object contains metals, its x-ray computed tomography (CT) images are normally affected by streaking artifacts. These artifacts are mainly caused by the x-ray beam hardening effects, which deviate the measurements from their true values. One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values. Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image. We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result. This paper proposes an iterative algorithm to optimize this objective function, and the unknowns are the metal affected projections. Once the metal affected projections are estimated, the filtered backprojection algorithm is used to reconstruct the final image. This paper applies the proposed algorithm to some airport bag CT scans. The bags all contain unknown metallic objects. The metal artifacts are effectively reduced by the proposed algorithm.


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.


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