3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction.

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed tomography (CT) imaging. We introduce a method that can reduce both artifacts simultaneously.

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.

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images.

Cone-beam dental CT can provide high-precision 3D images of the teeth and surrounding bones. From the 3D CT images, 3D models, also called digital impressions, can be computed for CAD/CAM-based fabrication of dental restorations or orthodontic devices. However, the cone-beam angle-dependent artifacts, mostly caused by the incompleteness of the projection data acquired in the circular cone-beam scan geometry, can induce significant errors in the 3D models.

Half-scan artifact correction using generative adversarial network for dental CT.

Half-scan image reconstruction with Parker weighting can correct motion artifacts in dental CT images taken with a slow scan-based dental CT. Since the residual half-scan artifacts in the dental CT images appear much stronger than those in medical CT images, the artifacts often persist to the extent that they compromise the surface-rendered bone and tooth images computed from the dental CT images. We used a variation of generative adversarial network (GAN), so-called U-WGAN, to correct half-scan artifacts in dental CT images.

Image denoising by transfer learning of generative adversarial network for dental CT.

The successful development of the image denoising techniques for low-dose computed tomography (LDCT) was largely owing to the public-domain availability of spatially-aligned high- and low-dose CT image pairs. Even though low-dose CT scans are also highly desired in dental imaging, public-domain databases of dental CT image pairs have not been established yet. In this paper, we propose a dental CT image denoising method based on the transfer learning of a generative adversarial network (GAN) from the public-domain CT images.

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.

A motion artifact reduction method for dental CT based on subpixel-resolution image registration of projection data.

High-resolution imaging is essential in three-dimensional (3D) CT image-based digital dentistry. A small amount of head motion during a CT scan can degrade the spatial resolution of the images to the extent where the efficacy of 3D image-based digital dentistry is greatly compromised. We introduce a retrospective motion artifact reduction (MAR) method for dental CTs that eliminates the necessity for any external motion tracking devices.

A head motion estimation algorithm for motion artifact correction in dental CT imaging.

A small head motion of the patient can compromise the image quality in a dental CT, in which a slow cone-beam scan is adopted. We introduce a retrospective head motion estimation method by which we can estimate the motion waveform from the projection images without employing any external motion monitoring devices. We compute the cross-correlation between every two successive projection images, which results in a sinusoid-like displacement curve over the projection view when there is no patient motion.

Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography.

Purpose: In a dental CT scan, the presence of dental fillings or dental implants generates severe metal artifacts that often compromise readability of the CT images. Many metal artifact reduction (MAR) techniques have been introduced, but dental CT scans still suffer from severe metal artifacts particularly when multiple dental fillings or implants exist around the region of interest. The high attenuation coefficient of teeth often causes erroneous metal segmentation, compromising the MAR performance.

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 Ring Artifact Correction Method: Validation by Micro-CT Imaging with Flat-Panel Detectors and a 2D Photon-Counting Detector.

We introduce an efficient ring artifact correction method for a cone-beam computed tomography (CT). In the first step, we correct the defective pixels whose values are close to zero or saturated in the projection domain. In the second step, we compute the mean value at each detector element along the view angle in the sinogram to obtain the one-dimensional (1D) mean vector, and we then compute the 1D correction vector by taking inverse of the mean vector.

Trabecular thickness measurement in cancellous bones: postmortem rat studies with the zoom-in micro-tomography technique.

Using the cross-sectional images taken with the zoom-in micro-tomography technique, we measured trabecular thicknesses of femur bones in postmortem rats. Since the zoom-in micro-tomography technique is capable of high resolution imaging of a small local region inside a large subject, we were able to measure the trabecular thickness without extracting bone samples from the rats. For the zoom-in micro-tomography, we used a micro-tomography system consisting of a micro-focus x-ray source, a 1248 x 1248 flat-panel x-ray detector and a precision scan mechanism.

X-ray micro-tomography system for small-animal imaging with zoom-in imaging capability.

Since a micro-tomography system capable of microm-resolution imaging cannot be used for whole-body imaging of a small laboratory animal without sacrificing its spatial resolution, it is desirable for a micro-tomography system to have local imaging capability. In this paper, we introduce an x-ray micro-tomography system capable of high-resolution imaging of a local region inside a small animal. By combining two kinds of projection data, one from a full field-of-view (FOV) scan of the whole body and the other from a limited FOV scan of the region of interest (ROI), we have obtained zoomed-in images of the ROI without any contrast anomalies commonly appearing in conventional local tomography.

A flat-panel detector based micro-CT system: performance evaluation for small-animal imaging.

A dedicated small-animal x-ray micro computed tomography (micro-CT) system has been developed to screen laboratory small animals such as mice and rats. The micro-CT system consists of an indirect-detection flat-panel x-ray detector with a field-of-view of 120 x 120 mm2, a microfocus x-ray source, a rotational subject holder and a parallel data processing system. The flat-panel detector is based on a matrix-addressed photodiode array fabricated by a CMOS (complementary metal-oxide semiconductor) process coupled to a CsI:T1 (thallium-doped caesium iodide) scintillator as an x-ray-to-light converter.