Observer Evaluation of a Metal Artifact Reduction Algorithm Applied to Head and Neck Cone Beam Computed Tomographic Images

Korpics, Mark; Sürücü, Murat; Mescioglu, Ibrahim; Alite, Fiori; Block, Alec M.; Choi, Mehee; Emami, Bahman; Harkenrider, Matthew M.; Roeske, John C.

doi:10.1016/j.ijrobp.2016.07.028

Cited by 17 publications

(13 citation statements)

References 25 publications

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“…It seems that the MAR algorithm is not effective in the diagnosis of fenestration and dehiscence defects around dental implants. However, this algorithm may be effective in some other clinical applications . As an illustration, in patients who had received image‐guided radiation therapy treatment for head and neck cancer, MAR algorithm was beneficial for decreasing the metal artifacts made by dental fillings and dental implants on CBCT images .…”

Section: Discussionmentioning

confidence: 99%

“…MAR algorithms use different procedures to reduce the metal artifact and most consider metal as missing data . The iterative reconstruction techniques in CBCT units pioneered the recovery of missing data from ruined original data and increased the quality of image, but they are difficult and time consuming . On the other hand, some MAR algorithms use the projection interpolation techniques, which remove the projection data passing through the metal.…”

Section: Discussionmentioning

confidence: 99%

“…However, this algorithm may be effective in some other clinical applications. 22,23 As an illustration, in patients who had received image-guided radiation therapy treatment for head and neck cancer, MAR algorithm was beneficial for decreasing the metal artifacts made by dental fillings and dental implants on CBCT images. 22 Meilinger et al 23 also proved the efficacy of their novel MAR algorithm in decreasing the artifacts of metal implants that were placed at the vertebral column or humerus near the elbow-joint.…”

Section: Discussionmentioning

confidence: 99%

“…14 The iterative reconstruction techniques in CBCT units pioneered the recovery of missing data from ruined original data 14 and increased the quality of image, but they are difficult and time consuming. 22 On the other hand, some MAR algorithms use the projection interpolation techniques, which remove the projection data passing through the metal. These techniques are efficient and effective 22 ; however, the disadvantages of these techniques are removal of all the attenuation information from high-density objects, which may lead to alteration of these objects.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, some MAR algorithms use the projection interpolation techniques, which remove the projection data passing through the metal. These techniques are efficient and effective ; however, the disadvantages of these techniques are removal of all the attenuation information from high‐density objects, which may lead to alteration of these objects . Additionally, although some MAR algorithms used are inbuilt algorithms of the CBCT unit and the operator was not able to modify them, some MAR algorithms were available as adjunctive software for main CBCT software and the users were given the permission to modify the MAR algorithm.…”

Section: Discussionmentioning

confidence: 99%

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The efficacy of metal artifact reduction (MAR) algorithm in cone‐beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

Sheikhi

Behfarnia

Mostajabi

et al. 2019

Journal of Periodontology

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Background The aim of the present study was to investigate the impact of metal artifact reduction (MAR) algorithm of cone‐beam computed tomography (CBCT) on the diagnostic accuracy of fenestration and dehiscence around dental implants. Methods Twenty‐three dental implants were placed adjacent to the dehiscence and 23 adjacent to the fenestration defects on bovine bone blocks. The blocks were scanned with CBCT unit in two modes, with and without MAR algorithm. The area under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were determined for all modes. Results For both defects, the Az values were higher in off MAR condition. The values of sensitivity, positive predictive value, negative predictive value, and accuracy, were higher in off MAR condition for both defects. However, the specificity in both defects in the two modes was equal. Conclusion The MAR algorithm in CBCT unit may not be helpful in increasing the diagnostic accuracy of fenestration and dehiscence around dental implants.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

The efficacy of metal artifact reduction (MAR) algorithm in cone‐beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

Sheikhi

Behfarnia

Mostajabi

et al. 2019

Journal of Periodontology

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Cone‐beam computed tomography

MacDonald¹

2019

Oral and Maxillofacial Radiology

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Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning

Liang

Bibault²,

Leroy

et al. 2021

Medical Physics

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To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). Methods: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. Results: The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83 AE 0.04, and 0.85 AE 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm AE 1.15 mm, and 2.98 mm AE 1.42 mm, respectively. Conclusions: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

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Observer Evaluation of a Metal Artifact Reduction Algorithm Applied to Head and Neck Cone Beam Computed Tomographic Images

Cited by 17 publications

References 25 publications

The efficacy of metal artifact reduction (MAR) algorithm in cone‐beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

The efficacy of metal artifact reduction (MAR) algorithm in cone‐beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

Cone‐beam computed tomography

Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning

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