A new CT metal artifacts reduction algorithm based on fractional-order sinogram inpainting

Zhang, Yi; Pu, YoungGun; Hu, Jinrong; Liu, Yan; Zhou, Jiliu

doi:10.3233/xst-2011-0300

Cited by 46 publications

(37 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inpainting methods are used to recover the background P \ in D by filling in the defective region D from its surrounding information. The commonly used inpainting methods are linear interpolation [14,34,40], higher-order polynomial interpolation [1,4,62], wavelets [71,72], Fourier transforms [38], normalized interpolation methods [42], and total variation [9] or fractional-order inpainting methods [70]. However, these interpolation methods suffer from inherent limitations since their methods do not take into account the mismatch of the restored P \ with the range of the Radon transform [42,45].…”

Section: Remark On Mar and Discussionmentioning

confidence: 99%

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Park

Choi

Seo

2017

Comm Pure Appl Math

View full text Add to dashboard Cite

Metal streak artifacts in X‐ray computerized tomography (CT) are characterized here using the notion of the wavefront set from microlocal analysis. The metal artifacts are caused mainly from the mismatch of the forward model of the filtered back‐projection; the presence of metallic subjects in an imaging subject violates the model's assumption of the CT sinogram data being the Radon transform of an image. The increasing use of metallic implants has increased demand for the reduction of metal artifacts in the field of dental and medical radiography. However, it is a challenging issue due to the serious difficulties in analyzing the X‐ray data, which depends nonlinearly on the distribution of the metallic subject. In this paper, we, for the first time, provide a mathematical analysis to characterize the structure of metal streaking artifacts. The metal streaking artifacts are produced along the line tangent to boundaries of the metal region touching at least two different boundaries. We also found a sufficient condition for the nonexistence of the metal streaking artifacts. © 2017 Wiley Periodicals, Inc.

show abstract

Section: Remark On Mar and Discussionmentioning

confidence: 99%

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Park

Choi

Seo

2017

Comm Pure Appl Math

View full text Add to dashboard Cite

show abstract

“…There are three main directions which could be explored in future. Firstly, we think there is great potential to apply our framework to other applications, such as in tomographic imaging with occlusions and heavy metal artefacts where the inpainting region is much smaller [22,23]. Secondly, we would like to find an alternative numerical algorithm with either faster practical convergence or one which is more capable of avoiding local minima in this non-convex landscape.…”

Section: Discussionmentioning

confidence: 99%

Directional sinogram inpainting for limited angle tomography

et al. 2019

View full text Add to dashboard Cite

In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microscopy and Mammography, physical limitations on acquisition lead to regions of data which cannot be sampled. Depending on the severity of the restriction, reconstructions can contain severe, characteristic, artefacts. Our model aims to address these artefacts by inpainting the missing data simultaneously with the reconstruction. Numerically, this problem naturally evolves to require the minimisation of a non-convex and non-smooth functional so we review recent work in this topic and extend results to fit an alternating (block) descent framework. We perform numerical experiments on two synthetic datasets and one Electron Microscopy dataset. Our results show consistently that the joint inpainting and reconstruction framework can recover cleaner and more accurate structural information than the current state of the art methods.

show abstract

“…Zhao et al proposed interpolating the wavelet coefficients of the projection data (Zhao et al, ). To obtain a better visual effect, partial differential equation (PDE) methods were introduced (Gu et al, ; Duan et al, ; Zhang et al, ). The inpainting method proposed by Gu et al is based on Euler's elastica and curvature (Gu et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Duan et al introduced a classical image inpainting method based on total variation (TV) for metal artifact reduction (MAR) (Duan et al, ). Zhang et al extended TV and curvature diffusions to fractional order to get more accurate results (Zhang et al, ). Projection completion with filtering has also been presented (Watzke and Kalender, ; Bal and Spies ).…”

Section: Introductionmentioning

confidence: 99%

A Novel Noniterative Metal Artifact Reduction Method Using Coherence Transport with Fast Marching in Computed Tomography

Zhang

et al. 2012

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

Metal artifact reduction (MAR) is still a challenging problem in current computed tomography reconstruction. Traditional methods usually use the unaffected projection data in the same degree to interpolate contaminated positions in the bins. The continuality and smoothness features become hard to fulfill. In this article, we propose a novel noniterative MAR method that uses coherence transport with fast marching. Two-dimensional information, including the neighboring bins, is used to inpaint the uncorrected sinogram with a special form of weighted average according to a sequence calculated by the fast marching method. The results of the experiments and the quantitative analysis demonstrate that the proposed method can effectively suppress the artifacts caused by metal with a high-attenuation coefficient. Compared with linear interpolation, the proposed method can introduce less streak artifacts and obtain more accurate structure information of organs. Figure 5. (a) Original image with metal implants. (b) The reconstructed image for Figure 5a using the proposed method. (c) Original image with bilateral implants in the pelvic region. (d) The reconstructed image for Figure 5c using the proposed method. (e) Original image with metal implants in cervical vertebra. (f) The reconstructed image for Figure 5e using the proposed method. , X-ray CT metal artifact reduction using wavelets: An application for imaging total hip prostheses,

show abstract

A new CT metal artifacts reduction algorithm based on fractional-order sinogram inpainting

Cited by 46 publications

References 34 publications

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Directional sinogram inpainting for limited angle tomography

A Novel Noniterative Metal Artifact Reduction Method Using Coherence Transport with Fast Marching in Computed Tomography

Contact Info

Product

Resources

About