2012
DOI: 10.1186/1687-6180-2012-93
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An improved method for the removal of ring artifacts in high resolution CT imaging

Abstract: In high resolution computed tomography (CT) using flat panel detectors, imperfect or defected detector elements cause stripe artifacts in sinogram which results in concentric ring artifacts in the image. Such ring artifacts obscure image details in the regions of interest of the image. In this article, novel techniques are proposed for the detection, classification, and correction of ring artifacts in the sinogram domain. The proposed method is suitable for multislice CT with parallel or fan beam geometry. It … Show more

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Cited by 29 publications
(19 citation statements)
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“…Before we describe our implementation of the different reconstruction methods, we briefly discuss two attenuation priors of the form (13), namely the nonnegativity prior (corresponding to nonnegativity constraints u i ≥ 0), and a combination of the nonnegativity prior and total variation (TV) regularization [24]. Both of these priors can be combined with the existing AMAP model (14), the proposed model (21), the WLS model (15) and the SWLS model (27).…”
Section: A Attenuation Priorsmentioning
confidence: 99%
“…Before we describe our implementation of the different reconstruction methods, we briefly discuss two attenuation priors of the form (13), namely the nonnegativity prior (corresponding to nonnegativity constraints u i ≥ 0), and a combination of the nonnegativity prior and total variation (TV) regularization [24]. Both of these priors can be combined with the existing AMAP model (14), the proposed model (21), the WLS model (15) and the SWLS model (27).…”
Section: A Attenuation Priorsmentioning
confidence: 99%
“…As reported by Rashid et al (2012), these techniques can be classified into two groups: sinogram pre-processing and reconstructed images postprocessing. The pre-processing methods aim at detecting and correcting the spurious lines in the sinogram before applying the reconstruction process, thus, rings do not form if the method succeeds.…”
Section: Related Workmentioning
confidence: 99%
“…However, it is known that every captured image is a degraded version of the original due to the frequent and inevitable errors generated during the imaging process (Pujar and Kunnur, 2010). Therefore, different types of degradation are observed on CT medical images, such as Gaussian noise (Andria et al, 2013), low contrast artifact (Tan et al, 2012), Gaussian blur (Sun et al, 2013), streak artifact (Yazdi and Beaulieu, 2008), metal artifact , ring artifact (Rashid et al, 2012), out-of-field artifact (Boas and Fleischmann, 2012) and limited-angle artifact .…”
Section: Introductionmentioning
confidence: 98%