Dental Computed Tomography Images Research Articles

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

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.

A Metal Artifact Reduction Method Using a Fully Convolutional Network in the Sinogram and Image Domains for Dental Computed Tomography.

The reconstruction quality of dental computed tomography (DCT) is vulnerable to metal implants because the presence of dense metallic objects causes beam hardening and streak artifacts in the reconstructed images. These metal artifacts degrade the images and decrease the clinical usefulness of DCT. Although interpolation-based metal artifact reduction (MAR) methods have been introduced, they may not be efficient in DCT because teeth as well as metallic objects have high X-ray attenuation. In this study, we investigated an effective MAR method based on a fully convolutional network (FCN) in both sinogram and image domains. The method consisted of three main steps: (1) segmentation of the metal trace, (2) FCN-based restoration in the sinogram domain, and (3) FCN-based restoration in image domain followed by metal insertion. We performed a computational simulation and an experiment to investigate the image quality and evaluated the effectiveness of the proposed method. The results of the proposed method were compared with those obtained by the normalized MAR method and the deep learning-based MAR algorithm in the sinogram domain with respect to the root-mean-square error and the structural similarity. Our results indicate that the proposed MAR method significantly reduced the presence of metal artifacts in DCT images and demonstrated better image performance than those of the other algorithms in reducing the streak artifacts without introducing any contrast anomaly.

A Mathematical Morphology-Based Filter for Noise Reduction and Detail Preservation in Low-Dose Dental CT Images.

During the acquisition on a low-dose radiation computed tomography (CT) scan, images are usually marked by heavy noise and undesired artifacts, which dramatically reduce its applicability in the image processing workflow. A noise reduction and detail preservation filter based on mathematical morphology is presented in this paper. The filter is geared to allow control of an opening operator followed by a systematic contrast limited adaptive histogram equalization (CLAHE) in conjunction with a reconstruction by dilation in last stage. A quantitative metric built on peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean-squared error (MSE) were applied to check noise reduction, detail preservation, and performance. The results obtained by the proposed filter were compared with those obtained in the literature, showing very good results: compared with the best-tested filter, the filter had a gain of 7.91% on PSNR, 7.57% on SSIM and 37.8% on MSE.

U-net based metal segmentation on projection domain for metal artifact reduction in dental CT.

Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.

Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography

The resolution of dental computed tomography (CT) images is limited by detector geometry, sensitivity, patient movement, the reconstruction technique and the need to minimize radiation dose. Recently, the use of convolutional neural network (CNN) architectures has shown promise as a resolution enhancement method. In the current work, two CNN architectures—a subpixel network and the so called U-net—have been considered for the resolution enhancement of 2-D cone-beam CT image slices of ex vivo teeth. To do so, a training set of 5680 cross-sectional slices of 13 teeth and a test set of 1824 slices of 4 structurally different teeth were used. Two existing reconstruction-based super-resolution methods using $\boldsymbol {\ell _{2}}$ -norm and total variation regularization were used for comparison. The results were evaluated with different metrics (peak signal-to-noise ratio, structure similarity index, and other objective measures estimating human perception) and subsequent image-segmentation-based analysis. In the evaluation, micro-CT images were used as ground truth. The results suggest the superiority of the proposed CNN-based approaches over reconstruction-based methods in the case of dental CT images, allowing better detection of medically salient features, such as the size, shape, or curvature of the root canal.

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

A Normalized Metal Artifact Reduction Method Using an Artifact-Reduced Prior for Dental Computed Tomography

In dental computed tomography (DCT), metal artifact reduction (MAR) is a critical issue to improve the clinical usefulness of DCT and remains a challenging problem. Although various MAR methods have been developed in medical CT, those methods may not work robustly in DCT because teeth themselves, as well as metallic objects, have high X-ray attenuation. In this study, we investigated an MAR method that was based on sinogram normalization interpolation with an artifact-reduced prior for DCT. The method consisted of three main steps: segmentation of a metal trace, generation of an artifact-reduced prior image, and sinogram completion followed by DCT reconstruction. We performed a computational simulation and performed an experiment on a teeth phantom with several metal inserts to validate the proposed method. With respect to the root-mean-square error and the structural similarity, we compared our results with the ones obtained by using the combined prior-based MAR (CP-MAR) method. Our results indicate that the proposed MAR method reduced metal artifacts considerably in DCT images and showed an image performance that was better than that obtained by using the state of the art method (CP-MAR) in reducing streak artifacts without introducing any contrast anomaly.

Sparse-View Reconstruction in Dental Computed Tomography by Using a Dictionary-Learning Based Method

In this study, we investigated sparse-view reconstruction in dental computed tomography (DCT) by using a dictionary-learning (DL)-based method to reduce excessive radiation dose to patients. In sparse-view DCT, only a small number (< 100) of projections, far less than what is required by the Nyquist sampling theory, are acquired from the imaging system and used for image reconstruction. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements (or atoms). We implemented a DL-based reconstruction algorithm and performed a systematic simulation and an experiment to evaluate the algorithm’s effectiveness for sparse-view reconstruction in DCT. DCT images were reconstructed using the three sparse-view projections of P30, P40, and P60, and their image qualities were quantitatively evaluated in terms of the intensity profile, the universal quality index, and the peak signal-to-noise ratio. The hardware system used in the experiment consisted of an X-ray tube, which was run at 90 kVp and 40 mA, and a flat-panel detector with a 388-μm pixel size. Our simulation and experimental results indicate that the DL-based method significantly reduced streak artifacts in the sparse-view DCT reconstruction when using P40, thus maintaining image quality.

Reduction of Metal Artifact in Dental Computed Tomography by Homomorphic Wavelet Filtering in Sinogram Domain

In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in order to project the CT data into the sinogram domain. The sinogram of data can be decomposed into its illumination and reflectance components by using the homomorphic wavelet filtering. The investigation of the CT images shows that the degradations caused by metallic artifacts appear mainly in the illumination component. Therefore, in our approach the corrupted illumination component is restored by using the apriori information driven from the previous artifact-free sections. The results show that the metal artifacts are considerably reduced without eliminating the important details of the CT images. The proposed method is also compared with other existing methods on a set of dental CT images. Comparisons show the superiority of the proposed method over other existing methods.

Tooth and Alveolar Bone Segmentation From Dental Computed Tomography Images.

Three-dimensional (3D) models of tooth-alveolar bone complex are needed in treatment planning and simulation for computer-aided orthodontics. Tooth and alveolar bone segmentation from computed tomography (CT) images is a fundamental step in reconstructing their models. Due to less application of alveolar bone in conventional orthodontic treatment which may cause undesired side effects, the previous studies mainly focused on tooth segmentation and reconstruction, and did not consider the alveolar bone. In this study, we proposed a method to implement both tooth and alveolar bone segmentation from dental CT images for reconstructing their 3D models. First, the proposed method extracted the connected region of tooth and alveolar bone from CT images using a global convex level set model. Then, individual tooth and alveolar bone are separated from the connected region based on Radon transform and a local level set model. The experimental results showed that the proposed method could successfully complete both the tooth and alveolar bone segmentation from CT images, and outperformed the state of the art tooth segmentation methods in terms of accuracy. This suggests that the proposed method can be used in reconstructing the 3D models of tooth-alveolar bone complex for precise treatment.

A bench-top micro-CT capable of simulating head motions.

Computational three-dimensional (3D) models of a dental structure generated from 3D dental computed tomography (CT) images are now widely used in digital dentistry. To generate precise 3D models, high-resolution imaging of the dental structure with a dental CT is required. However, a small head motion of the patient during the dental CT scan could degrade the spatial resolution of CT images to the extent that digital dentistry is no longer possible. A bench-top micro-CT has been built to evaluate the head motion effects on the dental CT images. A micro-CT has been built on an optic table with a micro-focus x-ray source and a flat-panel detector. A rotation stage, placed in between the x-ray source and the detector, is mounted on two-directional goniometers that can rotate the rotation stage in two orthogonal directions while the rotation stage is performing the CT scan. The goniometers can make object motions of an arbitrary waveform to simulate head tilting or head nodding. CT images of a phantom have been taken with and without introducing the motions, and the motion effects on the CT images have been evaluated. Object motions parallel to the detector plane have greater effects on the CT images than those against the detector plane. With the bench-top micro-CT, the motion effects have been visually seen at a tiny rotational motion as small as 0.3°. The bench-top micro-CT can be used to evaluate head motion effects on the dental CT images. The projection data, taken with the motion effects, would be used to develop motion artifact correction methods for a high-resolution dental-CT.

An automatic panoramic image reconstruction scheme from dental computed tomography images.

Panoramic images of the jaws are extensively used for dental examinations and/or surgical planning because they provide a general overview of the patient's maxillary and mandibular regions. Panoramic images are two-dimensional projections of three-dimensional (3D) objects. Therefore, it should be possible to reconstruct them from 3D radiographic representations of the jaws, produced by CBCT scanning, obviating the need for additional exposure to X-rays, should there be a need of panoramic views. The aim of this article is to present an automated method for reconstructing panoramic dental images from CBCT data. The proposed methodology consists of a series of sequential processing stages for detecting a fitting dental arch which is used for projecting the 3D information of the CBCT data to the two-dimensional plane of the panoramic image. The detection is based on a template polynomial which is constructed from a training data set. A total of 42 CBCT data sets of real clinical pre-operative and post-operative representations from 21 patients were used. Eight data sets were used for training the system and the rest for testing. The proposed methodology was successfully applied to CBCT data sets, producing corresponding panoramic images, suitable for examining pre-operatively and post-operatively the patients' maxillary and mandibular regions.

Metal Artifact Reduction in Dental Computed Tomography Images Based on Sinogram Segmentation Using Curvelet Transform Followed by Hough Transform

In X-ray computed tomography (CT), the presence of metal objects in a patient's body produces streak artifacts in the reconstructed images. During the past decades, many different methods were proposed for the reduction or elimination of the streaking artifacts. When scanning a patient, the projection data affected by metal objects (missing projections) appear as regions with high intensities in the sinogram. In spiral fan beam CT, these regions are sinusoid-like curves on sinogram. During the first time, if the metal curves are detected carefully, then, they can be replaced by corresponding unaffected projections using other slices or opposite views; therefore, the CT slices regenerated by the modified sonogram will be imaged with high quality. In this paper, a new method of the segmentation of metal traces in spiral fan-beam CT sinogram is proposed. This method is based on a sinogram curve detection using a curvelet transform followed by 2D Hough transform. The initial enhancement of the sinogram using modified curvelet transform coefficients is performed by suppressing all the coefficients of one band and applying 2D Hough transform to detect more precisely metal curves. To evaluate the performance of the proposed method for the detection of metal curves in a sinogram, precision and recall metrics are calculated. Compared with other methods, the results show that the proposed method is capable of detecting metal curves, with better precision and good recovery.

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

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.

Computer aided detection of oral lesions on CT images

Oral lesions are important findings on computed tomography (CT) images. In this study, a fully automatic method to detect oral lesions in mandibular region from dental CT images is proposed. Two methods were developed to recognize two types of lesions namely (1) Close border (CB) lesions and (2) Open border (OB) lesions, which cover most of the lesion types that can be found on CT images. For the detection of CB lesions, fifteen features were extracted from each initial lesion candidates and multi layer perceptron (MLP) neural network was used to classify suspicious regions. Moreover, OB lesions were detected using a rule based image processing method, where no feature extraction or classification algorithm were used. The results were validated using a CT dataset of 52 patients, where 22 patients had abnormalities and 30 patients were normal. Using non-training dataset, CB detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Furthermore, OB detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework, which consists of two methods, has the potential to be used in clinical context, and assist radiologists for better diagnosis.

Combining volumetric dental CT and optical scan data for teeth modeling

Dental computer-aided design (CAD) systems have been intensively introduced to digital dentistry in recent years. As basic digital models, volumetric computed tomography (CT) images or optical surface scan data are used in most dental fields. In many fields, including orthodontics, complete teeth models are required for the diagnosis, planning and treatment purposes. In this research, we introduce a novel modeling approach combining dental CT images and an optically scanned surface to create complete individual teeth models. First, to classify crown and root regions for each set of data, corresponding pairs between two different data are determined based on their spatial relationship. The pairs are used to define the co-segmentation energy by introducing similarity and dissimilarity terms for each corresponding pair. Efficient global optimization can be performed by formulating a graph-cut problem to find the segmentation result that minimizes the energy. After classifying crown and root regions for each data set, complete individual teeth are obtained by merging the two different data sets. The advancing front method was successfully applied for merging purposes by considering the signed distance from the crown boundary of the surface mesh to the root surface of the CT. The teeth models which have detailed geometries obtained from the optically scanned surface and interstice regions recovered from volumetric data can be obtained using the proposed method. In addition, the suggested merging approach makes it possible to obtain complete teeth models from incomplete CT data with metal artifacts.

Usefulness of mandibular third molar coronectomy assessed through clinical evaluation over three years of follow-up

The aim of this study was to investigate the 3-year morbidity of coronectomy of the lower third molar and to monitor the behaviour and migration pattern of the retained roots postoperatively. A total of 92 patients (111 teeth) who had undergone a coronectomy between October 2005 and July 2009 were investigated. Patients were followed up at 3 months and 1, 2, and 3 years for clinical evaluation and dental computed tomography imaging of the coronectomy sites. In total, 10 cases (9%) required tooth root extraction within the 3 years after coronectomy. In seven of them, the distal pocket of the lower second molars remained connected to the roots within the first year. Of the cases in whom a pocket did not remain at an early stage, none showed peri-apical lesions on transmission images of the retained roots in the apical area, which usually result from necrosis of the pulp. Root migration increased in the first 2 years after coronectomy but stabilized between the second and third years. In addition, a significant difference was noted in root migration between patients of different ages and sex. Retained roots after coronectomy in the lower third molars led to no complications in terms of infection or the development of pathologies within the first 3 years postoperatively.

Use of recent diagnostic methods in locating multiple canals: A case series of six canals in maxillary first molar

AbstractThe root canal anatomy of maxillary first molars has been described as three roots with three canals, and the commonest variation is the presence of a second mesiobuccal canal. However, some unusual number of canals should always be expected. Careful examination of the pulpal floor is recommended to locate all the canals and should be confirmed with conventional radiographic methods and additional imaging systems. The case reports describe the successful endodontic treatment of two maxillary first molars with six canals, with the help of dental computed tomography imaging system.

Automatic Teeth Axes Calculation for Well-Aligned Teeth Using Cost Profile Analysis Along Teeth Center Arch

In dental implantology and virtual dental surgery planning using computed tomography (CT) images, the examination of the axes of neighboring and/or biting teeth is important to improve the performance of the masticatory system as well as the aesthetic beauty. However, due to its high connectivity to neighboring teeth and jawbones, a tooth and/or its axis is very elusive to automatically identify in dental CT images. This paper presents a novel method of automatically calculating individual teeth axes. The planes separating the individual teeth are automatically calculated using cost profile analysis along the teeth center arch. In this calculation, a novel plane cost function, which considers the intensity and the gradient, is proposed to favor the teeth separation planes crossing the teeth interstice and suppress the possible inappropriately detected separation planes crossing the soft pulp. The soft pulp and dentine of each individually separated tooth are then segmented by a fast marching method with two newly proposed speed functions considering their own specific anatomical characteristics. The axis of each tooth is finally calculated using principal component analysis on the segmented soft pulp and dentine. In experimental results using 20 clinical datasets, the average angle and minimum distance differences between the teeth axes manually specified by two dentists and automatically calculated by the proposed method were 1.94° ± 0.61° and 1.13 ± 0.56 mm, respectively. The proposed method identified the individual teeth axes accurately, demonstrating that it can give dentists substantial assistance during dental surgery such as dental implant placement and orthognathic surgery.

Automatic Extraction of Inferior Alveolar Nerve Canal Using Feature-Enhancing Panoramic Volume Rendering

Dental implant surgery, which involves the surgical insertion of a dental implant into the jawbone as an artificial root, has become one of the most successful applications of computed tomography (CT) in dental implantology. For successful implant surgery, it is essential to identify vital anatomic structures such as the inferior alveolar nerve (IAN), which should be avoided during the surgical procedure. Due to the ambiguity of its structure, the IAN is very elusive to extract in dental CT images. As a result, the IAN canal is typically identified in most previous studies. This paper presents a novel method of automatically extracting the IAN canal. Mental and mandibular foramens, which are regarded as the ends of the IAN canal in the mandible, are detected automatically using 3-D panoramic volume rendering (VR) and texture analysis techniques. In the 3-D panoramic VR, novel color shading and compositing methods are proposed to emphasize the foramens and isolate them from other fine structures. Subsequently, the path of the IAN canal is computed using a line-tracking algorithm. Finally, the IAN canal is extracted by expanding the region of the path using a fast marching method with a new speed function exploiting the anatomical information about the canal radius. In experimental results using ten clinical datasets, the proposed method identified the IAN canal accurately, demonstrating that this approach assists dentists substantially during dental implant surgery.