A Two-Stage Approach for Beam Hardening Artifact Reduction in Low-Dose Dental CBCT

Bayaraa, Taigyntuya; Hyun, Chang Min; Jang, Tae Jun; Lee, Sung Min; Seo, Jin Keun

doi:10.1109/access.2020.3044981

Cited by 17 publications

(7 citation statements)

References 46 publications

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“…To reduce metal artifacts in dental CT images, we use the concept of the direct sinogram correction (DSC) method [ 36 , 37 ]. The key idea of the DSC method is mapping the original projection data

, i.e., the measured projection data along a projection line, to a new projection data

in such a way that the new projection data set better satisfy the Radon transform.…”

Section: Methodsmentioning

confidence: 99%

“…They reported that the DSC method works well in the cases of phantom images and mathematically generated human images [ 36 ], but we found that the DSC method fails to correct heavy metal artifacts frequently appearing in clinical dental CT images. The same group proposed another method that uses deep learning as a second stage correction to overcome the limitations of the DSC method [ 37 ]. Yet, the new method needs a huge number of patient images, both artifact-free and artifact-corrupted, which were not available in the public domain until now.…”

Section: Introductionmentioning

confidence: 99%

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Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting

Hegazy¹,

Cho²,

Cho

et al. 2023

Sensors

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Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.

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, i.e., the measured projection data along a projection line, to a new projection data

in such a way that the new projection data set better satisfy the Radon transform.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting

Hegazy¹,

Cho²,

Cho

et al. 2023

Sensors

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“…Figure 8 and table 1 show qualitative and quantitative performance comparisons of the proposed network with linear interpolation, an image domain network, a sinogram domain network, and a sinogram inpainting network. For the linear interpolation, the sinogram reflection technique reported by Bayaraa et al (2020) was applied to deal with metal trace truncation. Image thresholding was used to extract metal traces.…”

Section: Test On Synthesized Cbct and Simulated Intra-oral Scan Datamentioning

confidence: 99%

“…metal, bone, tissue, air) (Schulze et al 2011, Gjesteby et al 2016. MAR is much more challenging in low-dose dental CBCT environment owing to offset detection, truncation of the field of view (FOV), low radiation dose, and 3D characteristics in image reconstruction (Bayaraa et al 2020). See figure 1.…”

Section: Introductionmentioning

confidence: 99%

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan

Hyun¹,

Bayaraa²,

Yun³

et al. 2022

Phys. Med. Biol.

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\textbf{Objective} Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning. However, the performance of existing metal artifact reduction (MAR) methods in handling the loss of the morphological structures of teeth in reconstructed CT images remains relatively limited. In this study, we developed an innovative MAR method to achieve optimal restoration of anatomical details. \textbf{Approach} The proposed MAR approach is based on a two-stage deep learning-based method. In the first stage, we employ a deep learning network that utilizes intra-oral scan data as side-inputs and performs multi-task learning of auxiliary tooth segmentation. The network is designed to improve the learning ability of capturing teeth-related features effectively while mitigating metal artifacts. In the second stage, a 3D bone-teeth-jaw model is constructed with weighted thresholding, where the weighting region is determined depending on the geometry of the intra-oral scan data. \textbf{Main results} The results of numerical simulations and clinical experiments are presented to demonstrate the feasibility of the proposed approach. \textbf{Significance} We propose for the first time a MAR method using radiation-free intra-oral scan data as supplemental information on the tooth morphological structures of teeth, which is designed to perform accurate 3D bone-teeth-jaw modeling in low-dose CBCT environments.

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“…This therefore leads to reconstructed images likely to be affected by noise and various artifacts in low-dose conditions. Nevertheless, the FDK algorithm is still the most commonly employed method in dental CBCT, either used alone in the case of highdose (Baba et al 2004) or associated to pre and/or post-processing to reduce some artifacts (metal in (Ibraheem et al 2012), beam hardening and noise in (Bayaraa et al 2020)) and to improve the image quality.…”

Section: Introductionmentioning

confidence: 99%

Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging

Friot-Giroux¹,

Peyrin²,

Maxim³

2022

Phys. Med. Biol.

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Objective Cone-Beam Computed Tomography is becoming more and more popular in applications such as 3D dental imaging. Iterative methods compared to the standard Feldkamp algorithm have shown improvements in image quality of reconstruction of low-dose acquired data despite their long computing time. An interesting aspect of iterative methods is their ability to include prior information such as sparsity constraints. While a large panel of optimization algorithms along with their adaptation to tomographic problems are available, they are mainly studied on 2D parallel or fan-beam data. The issues raised by 3D CBCT and moreover by truncated projections are still poorly understood. Approach We compare different carefully designed optimization schemes in the context of realistic 3D dental imaging. Besides some known algorithms, SIRT-TV and MLEM, we investigate the primal-dual hybrid gradient (PDHG) approach and a newly proposed MLEM-TV optimizer. The last one is alternating EM steps and TV-denoising,combination not yet investigated for CBCT. Experiments are performed on both simulated data from a 3D jaw phantom and data acquired with a dental clinical scanner. Main results With some adaptations to the specificities of CBCT operators, PDHG and MLEM-TV algorithms provide the best reconstruction quality. These results were obtained by comparing the full-dose image with a low-dose image and an ultra low-dose image. Significance The convergence speed of the original iterative methods is hampered by the conical geometry and significantly reduced compared to parallel geometries. We promote the pre-conditioned version of PDHG and we propose a pre-conditioned version of the MLEM-TV algorithm. To the best of our knowledge, this is the first time PDHG and convergent MLEM-TV algorithms are evaluated on experimental dental CBCT data, where constraints such as projection truncation and presence of metal have to be jointly overcome.

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A Two-Stage Approach for Beam Hardening Artifact Reduction in Low-Dose Dental CBCT

Cited by 17 publications

References 46 publications

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan

Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging

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