CT Dental Artifact: Comparison of an Iterative Metal Artifact Reduction Technique with Weighted Filtered Back-Projection

Diehn, Felix E.; Michalak, Gregory J.; DeLone, David R.; Kotsenas, Amy L.; Lindell, E. Paul; Campeau, Norbert G.; Halaweish, Ahmed F.; McCollough, Cynthia H.; Fletcher, Joel G.

doi:10.1177/2058460117743279

Cited by 32 publications

(30 citation statements)

References 14 publications

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“…Thereafter, a new reconstruction is performed based on the corrected sinogram, which results in a reduced incidence of metal artifacts in the reconstructed CBCT scan . However, the performance of such MAR methods depends strongly on the quality of the initial metal artifact segmentation, and is often limited by the introduction of secondary artifacts and incomplete metal artifact correction . As a consequence, metal artifacts remain a challenge in CAS.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

et al. 2019

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Purpose: In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this study aimed to develop a mixed-scale dense convolutional neural network (MS-D network) for bone segmentation in CBCT scans affected by metal artifacts. Method: Training data were acquired from 20 dental CBCT scans affected by metal artifacts. An experienced medical engineer segmented the bony structures in all CBCT scans using global thresholding and manually removed all remaining noise and metal artifacts. The resulting gold standard segmentations were used to train an MS-D network comprising 100 convolutional layers using far fewer trainable parameters than alternative convolutional neural network (CNN) architectures. The bone segmentation performance of the MS-D network was evaluated using a leave-2-out scheme and compared with a clinical snake evolution algorithm and two state-of-the-art CNN architectures (U-Net and ResNet). All segmented CBCT scans were subsequently converted into standard tessellation language (STL) models and geometrically compared with the gold standard. Results: CBCT scans segmented using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean Dice similarity coefficients of 0.87 AE 0.06, 0.87 AE 0.07, 0.86 AE 0.05, and 0.78 AE 0.07, respectively. The STL models acquired using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean absolute deviations of 0.44 mm AE 0.13 mm, 0.43 mm AE 0.16 mm, 0.40 mm AE 0.12 mm and 0.57 mm AE 0.22 mm, respectively. In contrast to the MS-D network, the ResNet introduced wave-like artifacts in the STL models, whereas the U-Net incorrectly labeled background voxels as bone around the vertebrae in 4 of the 9 CBCT scans containing vertebrae. Conclusion: The MS-D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts.

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Section: Introductionmentioning

confidence: 99%

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

et al. 2019

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“…2,15,16 Further studies, using anthropomorphic phantoms, also demonstrated the efficacy of iMAR. 17,18 Patient-based studies with iMAR are only rarely described in the literature, 4,19 and only a few retrospective studies with iMAR on real patients have been published so far, [20][21][22] albeit no contouring studies for the pelvis or head and neck (H & N) region have been performed. We used the iMAR algorithm for our study.…”

Section: Introductionmentioning

confidence: 99%

Clinical relevance of metal artefact reduction in computed tomography (iMAR) in the pelvic and head and neck region: Multi‐institutional contouring study of gross tumour volumes and organs at risk on clinical cases

Hagen

Kretschmer²,

Würschmidt³

et al. 2019

J Med Imag Rad Onc

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Introduction Artefacts caused by dental implants and hip replacements may impede target volume definition and dose calculation accuracy. The iterative metal artefact reduction (iMAR) algorithm can provide a solution for this problem. The present study compares delineation of gross tumour volumes (GTVs) and organs at risk (OARs) in the pelvic and the head and neck (H & N) regions using computed tomography (CT) with and without iMAR, and thus the practical applicability of iMAR for routine clinical use. Methods The native planning CT and CT‐iMAR data of two typical clinical cases with image‐distorting artefacts were used for multi‐institutional contouring and analysis using the Dice similarity coefficient (DSC). GTV/OAR contours were compared with an intraobserver approach and compared to predefined reference structures. Results Mean volume for GTVprostate in the intraobserver approach decreased from 87 ± 44 cm3 (native CT) to 75 ± 22 cm3 (CT‐iMAR) (P = 0.168). Compared to the reference, DSC values for GTVProstate increased from 0.68 ± 0.15 to 0.78 ± 0.07 (CT vs. iMAR) (P < 0.05). In the H & N region, the reference for GTVTongue (34 cm3) was underestimated on both data sets. No significant improvement in DSC values (0.83 ± 0.06 (native CT) versus 0.86 ± 0.06 (CT‐iMAR)) was observed. Conclusion The use of iMAR improves the anatomical delineation at the transition of prostate and bladder in cases of bilateral hip replacement. In the H & N region, anatomical residual structures and experience were apparently sufficient for precise contouring.

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“…Prior studies indicated a superior reduction of dental artifacts caused by dental hardware or diverse maxillofacial metal implants when several MAR algorithms from major vendors were used, compared with standard reconstruction. [24][25][26][27][28][29] However, MAR algorithms may introduce new artifacts into the image. These new artifacts can appear as defects or blurring around metal hardware in the bone window.…”

Section: Discussionmentioning

confidence: 99%

“…These new artifacts can appear as defects or blurring around metal hardware in the bone window. 26,27 Recently, several clinical studies reported that the combination of spectral detector CT (or dualenergy CT) with virtual monoenergetic images and MAR provided optimal artifact reduction and improved diagnostic imaging assessments in patients with dental implants and bridges or metallic dental prostheses. 36,37 As noted previously, the MBIR algorithm is a revolutionary reconstruction technology that uses various models and repeats the subtraction of original raw data after forward projection to yield a reconstructed image that differs minimally from the raw data.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity

Kubo

Ito

Sone

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Metal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image quality in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm. MATERIALS AND METHODS: This retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 6 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolution CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative reconstruction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction þ metal artifact reduction algorithms and model-based iterative reconstruction þ metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients' conditions assessed the image-quality scores of metal artifact reduction and structural depictions. RESULTS: Hybrid iterative reconstruction þ metal artifact reduction algorithms and model-based iterative reconstruction þ metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6-14.7; P , .01). The latter algorithms resulted in significant reduction in metal artifacts and significantly improved structural depictions (P , .01). CONCLUSIONS: Model-based iterative reconstruction þ metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images. ABBREVIATIONS: IR ¼ iterative reconstruction; hybrid-IRþMAR ¼ combination of hybrid iterative reconstruction and metal artifact reduction algorithms; MAR ¼ metal artifact reduction algorithms; MBIR ¼ model-based iterative reconstruction; MBIRþMAR ¼ combination of model-based iterative reconstruction and metal artifact reduction algorithms; U-HRCT ¼ ultra-high-resolution CT

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CT Dental Artifact: Comparison of an Iterative Metal Artifact Reduction Technique with Weighted Filtered Back-Projection

Cited by 32 publications

References 14 publications

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

Clinical relevance of metal artefact reduction in computed tomography (iMAR) in the pelvic and head and neck region: Multi‐institutional contouring study of gross tumour volumes and organs at risk on clinical cases

Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity

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