A Parameter Division Based Method for the Geometrical Calibration of X-Ray Industrial Cone-Beam CT

Xiao, Kai; Han, Yu; Xi, Xiaoqi; Yan, Bin; Bu, Haibing; Li, Lei

doi:10.1109/access.2018.2865124

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…The input of the algorithm for generating sinograms based on expressions (3)-( 8) receives an ideal sinogram. This sinogram for the test object, consisting of a finite number of fragments with different densities, is described by expression (11). For a detailed implementation of the algorithm being carried out for specific shapes of fragments of sections of the test object, for example, circles and squares, see ( 12)- (17).…”

Section: Formation Of Real Sinograms and Their Graphic Displaymentioning

confidence: 99%

See 1 more Smart Citation

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Osipov

Yadrenkin

Чахлов

et al. 2021

Russ J Nondestruct Test

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Abstract— A calculation model of X-ray computed tomography with a density assessment function in the geometry of a parallel beam has been proposed. The model includes blocks for simulating and correcting sinograms and reconstructing section images. When generating sinograms, the parameters of the test object, source, and recorder of X-ray radiation have been taken into account. Modeling algorithms are implemented in the MathCad system and tested on virtual test objects.

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Section: Formation Of Real Sinograms and Their Graphic Displaymentioning

confidence: 99%

“…[5][6][7][8]. The tasks of designing computed tomography systems are determined by consumer interests [9][10][11]. The relevant information is divided into two groups.…”

Section: Introductionmentioning

confidence: 99%

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Osipov

Yadrenkin

Чахлов

et al. 2021

Russ J Nondestruct Test

View full text Add to dashboard Cite

show abstract

“…The online method directly recovers unknown geometric parameters from projections of the imaged object. Researchers usually exploit the symmetrical properties of the mirrored projection [6] or sinogram [7] and consistency of different projections on the same virtual plane [8]. Jailin et al [9] further uses reprojection of a reconstructed 3D image close to the initial projection to recover the unknown geometric parameters.…”

Section: Related Workmentioning

confidence: 99%

“…In general, calibration methods can be categorized into two groups: online self-calibration and offline phantom calibration. Online methods perform calibration depending on the projection data [5][6][7][8] or reconstructed image of an object [9][10][11][12], while offline methods [13][14][15][16][17][18] leverage one view or multi-view projections of a specific calibration phantom. Online methods do not require a separate scan of a dedicated phantom, but the need for reconstruction images in some calibration methods increases the high computation burden.…”

Section: Introductionmentioning

confidence: 99%

A geometric calibration approach for an industrial cone-beam CT system based on a low-rank phantom

Yu¹,

Lü²,

Sun³

et al. 2021

Meas. Sci. Technol.

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Precise alignment of the system scan geometry is crucial to ensure the reconstructed image quality in the cone-beam CT system. A calibration method that depends on the local feature of ball bearings phantom and point-like markers is probably affected by local image variations. Besides, multiple projections with circular scanning are usually required by this type of method to derive misaligned parameters. In contrast to previous works, this paper proposes a method that depends on the global symmetric low-rank feature of a novel phantom, which can accurately represent the system geometrical misalignment. All the misaligned parameters of the cone-beam CT system can be estimated from a single perspective direction without circular scanning. Meanwhile, since the global low-rank feature of the phantom is utilized, the proposed method is robust to the noise. Extensive simulations and real experiments validate the accuracy and robustness of our method, which achieves better performance compared to an existing phantom-based method.

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“…In literature there are various methods described for reference-based and reference-less (off-line and on-line) misalignment compensation for cone-beam CT; see e.g. [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the impact of faulty scanning trajectories in robot-based x-ray computed tomography

Hiller¹,

Landstorfer²,

Marx³

et al. 2020

Meas. Sci. Technol.

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X-ray computed tomography (CT) imaging for industrial applications is limited to certain physical conditions to be fulfilled. The size of the measuring object and the accumulated wall thickness are two fundamental conditions. An omission of these conditions by not capturing object attenuation information by the x-ray detector leads to missing data in the 3D reconstruction process and results as a consequence in image degradation and artifacts. Conventional industrial x-ray CT is based on cone-beam projections and circular or helical scanning trajectories using linear axis and a rotary (lift) table. For many inspection tasks on big-sized or unusually shaped objects the physical limits for obtaining a sufficient high image quality are reached very quickly when using conventional CT systems. Industrial six-axis robots offer much more flexibility with respect to the conditions mentioned earlier and can overcome the limitations of conventional scanners. In the present work we characterized an industrial six-axis robot in its working space following ISO 9283 in terms of pose accuracy and pose repeatability. These results are then used to simulate faulty scanning trajectories in terms of pose deviations where a single robot is used as an object manipulator to rotate virtual specimens on a circular trajectory resulting in different (faulty) reconstruction datasets. These datasets are evaluated visually and by using performance parameters and geometrical features in order to determine the reproduction fidelity (performance) of a one arm robot-based CT system depending on different pose errors. With the results obtained it was shown that a robot-based CT system of type B (in our classification scheme) using one robot as object manipulator should be able to reach a spatial resolution power in the range of the voxel size (in our case 200 µm) and smaller (neglecting effects from focal spot size, detector unsharpness from x-ray to light conversation and scatter radiation) if systematic pose errors are compensated using appropriate calibration methods.

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A Parameter Division Based Method for the Geometrical Calibration of X-Ray Industrial Cone-Beam CT

Cited by 8 publications

References 17 publications

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

A geometric calibration approach for an industrial cone-beam CT system based on a low-rank phantom

Evaluation of the impact of faulty scanning trajectories in robot-based x-ray computed tomography

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