Metal artifact reduction in CT using fusion based prior image

Jun, Wang; Wang, Shijie; Chen, Yang; Wu, Jiasong; Coatrieux, Jean-Louis; Luo, Limin

doi:10.1118/1.4812424

Cited by 60 publications

(47 citation statements)

References 28 publications

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“…In most MAR algorithms, global thresholding is used to identify metal regions due to its computational efficiency [12]. However, small errors in the metal segmentation in the projection image would lead to residual streak artifacts in the reconstructed images.…”

Section: Methodsmentioning

confidence: 99%

“…The prior image is calculated by weighted summation of and as demonstrated in the Wang’s work [12]. For the weighted summation, the difference between the streak-free image and the metal-free image is computed by D ( x , y ) = I sf ( x , y ) − I mf ( x , y ).…”

Section: Methodsmentioning

confidence: 99%

“…After identifying the metal trace from the projection data, we replace the pixel values at the metal trace with the ones computed from the prior images. For the prior image generation, we use the recently introduced method, so called fusion prior-based MAR (FP-MAR) [12]. To validate the proposed method, we have performed imaging studies of dental phantoms using a micro-CT as well as computational simulation studies.…”

Section: Introductionmentioning

confidence: 99%

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A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

2016

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BackgroundMetal artifacts appearing as streaks and shadows often compromise readability of computed tomography (CT) images. Particularly in a dental CT in which high resolution imaging is crucial for precise preparation of dental implants or orthodontic devices, reduction of metal artifacts is very important. However, metal artifact reduction algorithms developed for a general medical CT may not work well in a dental CT since teeth themselves also have high attenuation coefficients.MethodsTo reduce metal artifacts in dental CT images, we made prior images by weighted summation of two images: one, a streak-reduced image reconstructed from the metal-region-modified projection data, and the other a metal-free image reconstructed from the original projection data followed by metal region deletion. To make the streak-reduced image, we precisely segmented the metal region based on adaptive local thresholding, and then, we modified the metal region on the projection data using linear interpolation. We made forward projection of the prior image to make the prior projection data. We replaced the pixel values at the metal region in the original projection data with the ones taken from the prior projection data, and then, we finally reconstructed images from the replaced projection data. To validate the proposed method, we made computational simulations and also we made experiments on teeth phantoms using a micro-CT. We compared the results with the ones obtained by the fusion prior-based metal artifact reduction (FP-MAR) method.ResultsIn the simulation studies using a bilateral prostheses phantom and a dental phantom, the proposed method showed a performance similar to the FP-MAR method in terms of the edge profile and the structural similarity index when an optimal global threshold was chosen for the FP-MAR method. In the imaging studies of teeth phantoms, the proposed method showed a better performance than the FP-MAR method in reducing the streak artifacts without introducing any contrast anomaly.ConclusionsThe simulation and experimental imaging studies suggest that the proposed method can be used for reducing metal artifacts in dental CT images.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

2016

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“…In an interesting observation from Wang et al, the prior images at the start of these iterative cycles to produce streak free images can be a source of defects to the iterative corrective process even with thresholding or linear interpolation MAR (LI-MAR) techniques used to suppress the metal. LI-MAR images although free of metal still contain streak artifact elements in the image data and iterative processing of this data is therefore seeded with incorrect information [29].…”

Section: Introductionmentioning

confidence: 99%

Suppression of the CT Beam Hardening Streak Artifact Using Predictive Correction on Detector Data

Stowe¹,

Curran²

2016

ujmsj

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The purpose of the research was to develop an automated program incorporating a predictive artifact correction technique (PACT) to correct for the signal deviations from metal beam hardening artifacts in Computed Tomography (CT) detector raw data. Thin-slice sequential CT scans were performed on a dosimetry head phantom using a Somatom Sensation 16 scanner to establish a ground truth image. Metal pins were then affixed to either side of the phantom at the three and nine o'clock positions to cause streak artifact in detector raw data and a subsequent streak image. The program automatically detected the extent of the overlap peaks in the detector raw data causing the artifact. It profiled a correction using adjacent projections so that the peak error could be corrected rather than simply being removed or smoothed by interpolation. The PACT algorithm modified raw data was then reconstructed on a SYNGO CT reconstruction workstation. This image was then compared against ground truth and that produced by commercially available metal artifact reduction projection completion and also a research based iterative technique. Qualitative results illustrate superior suppression of streak artifact in images using PACT when compared directly to tested projection completion methods but inferior to iterative reconstruction. Recovery of voxel data underlying the streak is also demonstrated to be quantitatively superior with PACT when referenced to the original ground truth image. Limitations were however detected with the threshold technique for initial localisation of the streak sources. The work still demonstrates the feasibility of this predictive artifact correction technique in correcting beam hardening affected voxel data without recourse to expensive additional options such as iterative reconstruction or dual energy that are not so commonly available in the clinical setting.

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“…Several methods have been developed to reduce metal artifacts and improve image quality in spiral computed tomography (CT) [6][7][8][9][10][11][12][13][14][15][16] and CBCT. [17][18][19][20][21][22][23] These metal artifact reduction (MAR) techniques are broadly classified as interpolative and iterative methods.…”

Section: Introductionmentioning

confidence: 99%

Metal Artifact Reduction in Cone-Beam Computed Tomography for Head and Neck Radiotherapy

Korpics

Johnson

Patel

et al. 2016

Technol Cancer Res Treat

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Purpose: To evaluate a method for reducing metal artifacts, arising from dental fillings, on cone-beam computed tomography images. Materials and Methods: A projection interpolation algorithm is applied to cone-beam computed tomography images containing metal artifacts from dental fillings. This technique involves identifying metal regions in individual cone-beam computed tomography projections and interpolating the surrounding values to remove the metal from the projection data. Axial cone-beam computed tomography images are then reconstructed, resulting in a reduction in the streak artifacts produced by the metal. Both phantom and patient imaging data are used to evaluate this technique. Results: The interpolation substitution technique successfully reduced metal artifacts in all cases. Corrected images had fewer or no streak artifacts compared to their noncorrected counterparts. Quantitatively, regions of interest containing the artifacts showed reduced variance in the corrected images versus the uncorrected images. Average pixel values in regions of interest around the metal object were also closer in value to nonmetal regions after artifact reduction. Artifact correction tended to perform better on patient images with less complex metal objects versus those with multiple large dental fillings. Conclusion: The interpolation substitution is potentially an efficient and effective technique for reducing metal artifacts caused by dental fillings on cone-beam computed tomography image. This technique may be effective in reducing such artifacts in patients with head and neck cancer receiving daily image-guided radiotherapy.

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Metal artifact reduction in CT using fusion based prior image

Cited by 60 publications

References 28 publications

A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation

Suppression of the CT Beam Hardening Streak Artifact Using Predictive Correction on Detector Data

Metal Artifact Reduction in Cone-Beam Computed Tomography for Head and Neck Radiotherapy

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