An interval subdividing based method for geometric calibration of cone-beam CT

Tan, Shengqi; Cong, Peng; Xi-ming, Liu; Wu, Zhifang

doi:10.1016/j.ndteint.2013.04.011

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…Consequently, f (h) reaches the maximum value when the second solution is chosen. Considering that the resolution of flatpanel detector is the width of one detector pixel (denoted by d pixel ), we assert that there is no geometry artifacts existing in reconstruction images if the maximum value of f (h) is lower than the width of one detector pixel [18,19]. As a consequence, h max can be expressed as a function with respect to variables n 1 and n 2 by letting f (h) = d pixel and substituting the second solution in Eq.…”

Section: Segmentation Methods Of Ascending Path In Helical Scanmentioning

confidence: 99%

“…In the first place, the FDK algorithm is computationally efficient and can give satisfactory results when the cone angle of X-ray source is smaller than 10 degrees. Secondly, geometric calibration for circular scan has been well solved by researchers [8][9][10][11][12][13][14][15][16][17][18][19]. In these methods, Noo's analytic method [8] is a classical method for circular scan which uses the parameters of two ellipses that are the projected images of two circles to determine the geometry parameters.…”

Section: Introductionmentioning

confidence: 99%

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Practical geometric calibration for helical cone-beam industrial computed tomography

Zhang¹,

Yan²,

Li³

et al. 2014

Journal of X-Ray Science and Technology

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In helical cone-beam industrial computed tomography (ICT), the reconstructed images may be interfered by geometry artifacts due to the presence of mechanical misalignments. To obtain artifact-free reconstruction images, a practical geometric calibration method for helical scan is investigated based on Noo's analytic geometric calibration method for circular scan. The presented method is implemented by first dividing the whole ascending path of helical scan into several pieces, then acquiring the projections of a dedicated calibration phantom in circular scan at each section point, of which geometry parameters are calculated using Noo's analytic method. At last, the geometry parameters of each projection in a piece can be calculated by those of the two end points of the piece. We performed numerical simulations and real data experiments to study the performance of the presented method. The experimental results indicated that the method can obtain high-precision geometry parameters of helical scan and give satisfactory reconstruction images.

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Section: Segmentation Methods Of Ascending Path In Helical Scanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical geometric calibration for helical cone-beam industrial computed tomography

Zhang¹,

Yan²,

Li³

et al. 2014

Journal of X-Ray Science and Technology

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“…Many methods have been proposed for CT geometric calibration [1,2,[5][6][7][8][9][10][11] , most of these methods require special manufactured calibration phantom for calculating the geometric parameters [5][6][7][8][9] . Noo et al [5] proposed an analytic method based on identification of ellipse parameters, they use the ellipse parameters as intermediate parameters to calculate the CT geometric parameters.…”

Section: Introductionmentioning

confidence: 99%

A wire scanning based method for geometric calibration of high resolution CT system

Jiang

et al. 2015

SPIE Proceedings

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This paper is about geometric calibration of the high resolution CT (Computed Tomography) system. Geometric calibration refers to the estimation of a set of parameters that describe the geometry of the CT system. Such parameters are so important that a little error of them will degrade the reconstruction images seriously, so more accurate geometric parameters are needed in the higher-resolution CT systems. But conventional calibration methods are not accurate enough for the current high resolution CT system whose resolution can reach sub-micrometer or even tens of nanometers. In this paper, we propose a new calibration method which has higher accuracy and it is based on the optimization theory. The superiority of this method is that we build a new cost function which sets up a relationship between the geometrical parameters and the binary reconstruction image of a thin wire. When the geometrical parameters are accurate, the cost function reaches its maximum value. In the experiment, we scanned a thin wire as the calibration data and a thin bamboo stick as the validation data to verify the correctness of the proposed method. Comparing with the image reconstructed with the geometric parameters calculated by using the conventional calibration method, the image reconstructed with the parameters calculated by our method has less geometric artifacts, so it can verify that our method can get more accurate geometric calibration parameters. Although we calculated only one geometric parameter in this paper, the geometric artifacts are still eliminated significantly. And this method can be easily generalized to all the geometrical parameters calibration in fan-beam or cone-beam CT systems.

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“…Subsequently, Kingston et al proposed a method based on the sharpness of reconstructed image maximization to obtain the sharpest three-dimensional (3D) reconstruction. [10] Very recently, a practical and robust method based on interval subdividing was researched by Tan et al [11] As is well known, reconstruction of an image with only the geometric artifacts is necessary for all self-calibration methods. Thus, selfcalibration for interior tomography is more difficult than that for global tomography because of the presence of projection data truncation artifacts.…”

Section: Introductionmentioning

confidence: 99%

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mentioning

confidence: 99%

A modified interval subdividing based geometric calibration method for interior tomography

Zhang¹,

Yan²,

Li³

et al. 2014

Chinese Phys. B

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The interior tomography is commonly met in practice, whereas the self-calibration method for geometric parameters remains far from explored. To determine the geometry of interior tomography, a modified interval subdividing based method, which was originally developed by Tan et al., [11] was presented in this paper. For the self-calibration method, it is necessary to obtain the reconstructed image with only geometric artifacts. Therefore, truncation artifacts reduction is a key problem for the self-calibration method of an interior tomography. In the method, an interior reconstruction algorithm instead of the Feldkamp-Davis-Kress (FDK) algorithm was employed for truncation artifact reduction. Moreover, the concept of a minimum interval was defined as the stop criterion of subdividing to ensure the geometric parameters are determined nicely. The results of numerical simulation demonstrated that our method could provide a solution to the selfcalibration for interior tomography while the original interval subdividing based method could not. Furthermore, real data experiment results showed that our method could significantly suppress geometric artifacts and obtain high quality images for interior tomography with less imaging cost and faster speed compared with the traditional geometric calibration method with a dedicated calibration phantom.

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An interval subdividing based method for geometric calibration of cone-beam CT

Cited by 12 publications

References 15 publications

Practical geometric calibration for helical cone-beam industrial computed tomography

Practical geometric calibration for helical cone-beam industrial computed tomography

A wire scanning based method for geometric calibration of high resolution CT system

A modified interval subdividing based geometric calibration method for interior tomography

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