Reduction of Metal Artifacts from Hip Prostheses on CT Images of the Pelvis: Value of Iterative Reconstructions

Morsbach, Fabian; Bickelhaupt, Sebastian; Wanner, Guido A.; Krauß, A.; Schmidt, Bernhard; Alkadhi, Hatem

doi:10.1148/radiol.13122089

Cited by 150 publications

(107 citation statements)

References 22 publications

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“…IMAR reduced metal artifacts significantly and improved number measurements with CT and the confidence in depicting pelvic abnormalities. 21 In our study, we found a significant improvement in softtissue delineation in the oral cavity and oropharynx, but we also found a degradation of bone delineation; artificial defects in the IMAR datasets of the bone abutting the metallic implants appeared in a number of cases (58%). Because of the reduction of streak artifacts, however, a better delineation of cortical bone at more remote areas in the sections containing metal hardware was achieved.…”

Section: Discussionsupporting

confidence: 56%

Improved Image Quality in Head and Neck CT Using a 3D Iterative Approach to Reduce Metal Artifact

Wuest

May

Brand

et al. 2015

AJNR Am J Neuroradiol

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Section: Discussionsupporting

confidence: 56%

Improved Image Quality in Head and Neck CT Using a 3D Iterative Approach to Reduce Metal Artifact

Wuest

May

Brand

et al. 2015

AJNR Am J Neuroradiol

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“…Since image noise is reduced compared to the traditional filtered back projection (FBP) reconstruction method, IR can be used in the clinical setting with two different major aims: firstly, to achieve reduction of patient dose exposure by reducing the exposure settings; secondly, to achieve a possible improvement in image quality by reduction of image noise and beam hardening artifacts [8 -18]. Other possible fields of applying IR include e. g. reduction of metal artifacts or scatter compensation, which may become even more important in the future [19,20]. Some recent studies have sought to demonstrate a potential effect of IR techniques in brain CT [8, 17,21].…”

mentioning

confidence: 99%

Intraindividual Evaluation of the Influence of Iterative Reconstruction and Filter Kernel on Subjective and Objective Image Quality in Computed Tomography of the Brain

Buhk

Laqmani

Schultzendorff

et al. 2013

Fortschr Röntgenstr

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Objectives: To intraindividually evaluate the potential of 4th generation iterative reconstruction (IR) on brain CT with regard to subjective and objective image quality. Methods: 31 consecutive raw data sets of clinical routine native sequential brain CT scans were reconstructed with IR level 0 (=?filtered back projection), 1, 3 and 4; 3 different brain filter kernels (smooth/standard/sharp) were applied respectively. Five independent radiologists with different levels of experience performed subjective image rating. Detailed ROI analysis of image contrast and noise was performed. Statistical analysis was carried out by applying a random intercept model. Results: Subjective scores for the smooth and the standard kernels were best at low IR levels, but both, in particular the smooth kernel, scored inferior with an increasing IR level. The sharp kernel scored lowest at IR 0, while the scores substantially increased at high IR levels, reaching significantly best scores at IR 4. Objective measurements revealed an overall increase in contrast-to-noise ratio at higher IR levels, which was highest when applying the soft filter kernel. The absolute grey-white contrast decreased with an increasing IR level and was highest when applying the sharp filter kernel. All subjective effects were independent of the raters? experience and the patients? age and sex. Conclusion: Different combinations of IR level and filter kernel substantially influence subjective and objective image quality of brain CT.

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“…To more practically reduce segmentation errors, Prell et al [45] suggested tissue classification on a CT image corrected by linear interpolation. This idea has recently motivated some recent attempts to iteratively improve the derivation of the prior image [61], [62]. There are also other approaches enabling to avoid segmentation errors and possibly to improve the accuracy of recovered projections by defining a prior image from statistical anatomical atlases [63].…”

Section: Discussionmentioning

confidence: 99%

3D Prior Image Constrained Projection Completion for X-ray CT Metal Artifact Reduction

Mehranian

Rahmim

et al. 2013

IEEE Trans. Nucl. Sci.

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Abstract-The presence of metallic implants in the body of patients undergoing X-ray computed tomography (CT) examinations often results in severe streaking artifacts that degrade image quality. In this work, we propose a new metal artifact reduction (MAR) algorithm for 2D fan-beam and 3D cone-beam CT based on the maximum a posteriori (MAP) completion of the projections corrupted by metallic implants. In this algorithm, the prior knowledge obtained from a tissue-classified prior image is exploited in the completion of missing projections and incorporated into a new prior potential function. The prior is especially designed to exploit and promote the sparsity of a residual projection (sinogram) dataset obtained from the subtraction of the unknown target dataset from the projection dataset of the tissue-classified prior image. The MAP completion is formulated as an equality-constrained convex optimization and solved using an accelerated projected gradient algorithm. The performance of the proposed algorithm is compared with two state-of-the-art algorithms, namely 3D triangulated linear interpolation (LI) and normalized metal artifact reduction (NMAR) algorithm using simulated and clinical studies. The simulations targeting artifact reduction in 2D fan-beam and 3D cone-beam CT demonstrate that our algorithm can outperform its counterparts, particularly in cone-beam CT. In the clinical datasets, the performance of the proposed algorithm was subjectively and objectively compared in terms of metal artifact reduction of a sequence of 2D CT slices. The clinical results show that the proposed algorithm effectively reduces metal artifacts without introducing new artifacts due to erroneous interpolation and normalization as in the case of LI and NMAR algorithms.Index Terms-Metal artifact reduction, prior image, X-ray CT, 3D projection completion.

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Reduction of Metal Artifacts from Hip Prostheses on CT Images of the Pelvis: Value of Iterative Reconstructions

Abstract: The IFS algorithm for CT image reconstruction significantly reduces metal artifacts from hip prostheses, improves the reliability of CT number measurements, and improves the confidence for depicting pelvic abnormalities.

Cited by 150 publications

References 22 publications

Improved Image Quality in Head and Neck CT Using a 3D Iterative Approach to Reduce Metal Artifact

Improved Image Quality in Head and Neck CT Using a 3D Iterative Approach to Reduce Metal Artifact

Intraindividual Evaluation of the Influence of Iterative Reconstruction and Filter Kernel on Subjective and Objective Image Quality in Computed Tomography of the Brain

3D Prior Image Constrained Projection Completion for X-ray CT Metal Artifact Reduction

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