2019
DOI: 10.1155/2019/2351878
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An Analytical Method for Reducing Metal Artifacts in X‐Ray CT Images

Abstract: Medical CT imaging often encounters metallic implants or some metal interventional therapy apparatus. These metallic objects can produce metal artifacts in reconstruction images, which severely degrade image quality. In this paper, we analyze the difference between polychromatic projection data and Radon transform data and develop an analytical method to reduce metal artifacts. Approximate features of metal artifacts can be obtained by a simplified energy spectrum function of x-ray beam. The developed method c… Show more

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Cited by 9 publications
(8 citation statements)
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“…If system (5) is D(q, r)-stable then there exist two constants C 1 > 0,C 2 > 0 such that C 2 x 0 2 ( q + ε) t ≤ E x(t) 2 ≤ C 1 x 0 2 (( q + r)ε) t . If system (5) is D(r, θ )-stable then there exists a constant C > 0 such that E x(t) 2 ≤ C x 0 2 (rε) t .Remark 3.3 From Theorem 3.5 and Corollaries 3.2-3.4, region stabilities defined in this paper imply mean square stability of system(5).…”
mentioning
confidence: 81%
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“…If system (5) is D(q, r)-stable then there exist two constants C 1 > 0,C 2 > 0 such that C 2 x 0 2 ( q + ε) t ≤ E x(t) 2 ≤ C 1 x 0 2 (( q + r)ε) t . If system (5) is D(r, θ )-stable then there exists a constant C > 0 such that E x(t) 2 ≤ C x 0 2 (rε) t .Remark 3.3 From Theorem 3.5 and Corollaries 3.2-3.4, region stabilities defined in this paper imply mean square stability of system(5).…”
mentioning
confidence: 81%
“…Stability is one of essential concepts in dynamical system theory, which has been considered by the researchers in many fields, such as [1][2][3][4][5][6][7][8], and [9][10][11][12]. For a linear system without delays, as is well known, the stability is related to the system matrix root-clustering in subregions of the complex plane.…”
Section: Introductionmentioning
confidence: 99%
“…Image restoration mainly includes image deblurring and image denoising, which is one of the most fundamental problems in imaging science. It plays an important role in many mid-level and high-level image-processing areas such as medical imaging, remote sensing, machine identification, and astronomy [1][2][3][4]. The image restoration problem usually can be expressed in the following form:…”
Section: Introductionmentioning
confidence: 99%
“…The Tikhonov regularization method has a disadvantage, which tends to make images overly smooth and often fails to adequately preserve important image attributes such as sharp edges. The total variation regularization method has the ability to preserve edges well and remove noise at the same time, which was first introduced by Rudin et al [6] as follows: min f Hfg 2 2 + α f TV , (1.2) where • 2 denotes the Euclidean norm, • TV is the discrete total variation regularization term, and α is a positive regularization parameter that controls the tradeoff between these two terms. To define the discrete TV norm, we first introduce the discrete gradient ∇f :…”
Section: Introductionmentioning
confidence: 99%
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