Dental Cone Beam Computed Tomography Research Articles

Preoperative assessment of bone density for dental implantation: a comparative study of three different ROI methods

BackgroundDental cone beam computed tomography (CBCT) is commonly used to evaluate cancellous bone density before dental implant surgery. However, to our knowledge, no measurement approach has been standardized yet. This study aimed to evaluate the relationship between three different regions of interest (ROI) methods on cancellous bone density at the dental implant site using dental CBCT images.MethodsPatients’ dental CBCT images (n = 300) obtained before dental implant surgery were processed using Mimics (Materialise, Leuven, Belgium). At the potential implant sites, the rectangle, cylinder, and surrounding cylinder ROI methods were used to measure bone density. Repeated measures one-way analysis of variance was performed to compare the three ROI methods in terms of measurement results. Pearson correlation analysis was performed to identify the likely pair-wise correlations between the three ROI methods.ResultsThe density value obtained using the surrounding cylinder approach (grayscale value [GV],523.56 ± 228.03) was significantly higher than the values obtained using the rectangle (GV, 497.04 ± 236.69) and cylinder (GV,493 ± 231.19) ROI methods in terms of results. Furthermore, significant correlations were noted between the ROI methods (r > 0.965; p < 0.001).ConclusionsThe density measured using the surrounding cylinder method was the highest. The choice of method may not influence the trends of measurement results.Trial registrationThis study was approved by the Institutional Review Board of China Medical University Hospital, No. CMUH111-REC3-205. Informed consent was waived by the Institutional Review Board of China Medical University Hospital, CMUH111-REC3-205, owing to the retrospective nature of the study.

AAPM Task Group Report 261: Comprehensive quality control methodology and management of dental and maxillofacial cone beam computed tomography (CBCT) systems.

Cone-beam computed tomography (CBCT) systems specifically designed and manufactured for dental, maxillofacial imaging (MFI) and otolaryngology (OLR) applications have been commercially available in the United States since 2001 and have been in widespread clinical use since. Until recently, there has been a lack of professional guidance available for medical physicists about how to assess and evaluate the performance of these systems and about the establishment and management of quality control (QC) programs. The owners and users of dental CBCT systems may have only a rudimentary understanding of this technology, including how it differs from conventional multidetector CT (MDCT) in terms of acceptable radiation safety practices. Dental CBCT systems differ from MDCT in several ways and these differences are described. This report provides guidance to medical physicists and serves as a basis for stakeholders to make informed decisions regarding how to manage and develop a QC program for dental CBCT systems. It is important that a medical physicist with experience in dental CBCT serves as a resource on this technology and the associated radiation protection best practices. The medical physicist should be involved at the pre-installation stage to ensure that a CBCT room configuration allows for a safe and efficient workflow and that structural shielding, if needed, is designed into the architectural plans. Acceptance testing of new installations should include assessment of mechanical alignment of patient positioning lasers and x-ray beam collimation and benchmarking of essential image quality performance parameters such as image uniformity, noise, contrast-to-noise ratio (CNR), spatial resolution, and artifacts. Several approaches for quantifying radiation output from these systems are described, including simply measuring the incident air-kerma (Kair) at the entrance surface of the image receptor. These measurements are to be repeated at least annually as part of routine QC by the medical physicist. QC programs for dental CBCT, at least in the United States, are often driven by state regulations, accreditation program requirements, or manufacturer recommendations.

Improvement of metal artifact reduction in dental CBCT using a CdTe photon-counting detector: Simulation study

In dental cone-beam computed tomography (CBCT), the image quality often degrades when patients have metallic objects such as metal implants and/or metal prostheses. Metal artifacts typically appear as streaks and shadows in the reconstructed CT images, limiting their clinical usefulness. Although various metal artifact reduction (MAR) methods are used in dental CBCT, no algorithm that can robustly remove metal artifacts has been universally accepted. Recent research has explored the potential benefits of using of a photon-counting detector (PCD) to advance enhanced MAR. PCD can classify x-ray photons into several different energy bins, allowing for more precise measurements of x-ray attenuation. Therefore, in this study, we propose a new MAR method based on the use of a PCD, named the high-energy trace normalized-MAR (HT-NMAR) algorithm, to ensure improved image quality. The proposed MAR algorithm consists of three main steps: 1) Segmentation of the metal trace using a high-energy binned sinogram, 2) Generation of a residual artifact-reduced prior, 3) Sinogram completion with the segmented metal trace and the prior, followed by CBCT reconstruction. We conducted a simulation on a numerical head phantom with metal inserts using a PCD simulation toolkit to demonstrate its efficacy. Our simulation study indicates that the proposed MAR method considerably reduces metal artifacts in CBCT and shows a better image performance than other existing MAR methods in reducing streak artifacts without any contrast anomaly.

Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging

PurposeClinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.MethodsKnee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images.ResultsThe SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available.ConclusionModel training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.

Assessment of Correlation between Dimensions of Ball Phantom and Distortion Rate of Panoramic Radiography in Dental Cone Beam Computed Tomography

The objective of this study was to assess correlation between dimensions of ball phantom and distortion rate of panoramic radiography in dental cone beam computed tomography (CBCT) system using appropriate standard methods. The results revealed that only two measurement (2.99%) out of 67 measurements exceeded the recommended threshold value for distortion rate. There was no correlation between the ball diameter and the distortion produced. However, a low positive correlation was observed between the distortion rate and the ratio of the horizontal to the vertical diameter of the ball images. Also, the matlab code, presented results which are closer to the acceptable limit than the Romexis software. Patient positioning should be carefully and correctly considered to prevent and to bring the image distortion rate to its barest minimum for proper diagnosis in dental CBCT. For proper diagnosis and treatment planning, this distortion must be accounted for during clinical applications.

A simplified low-cost phantom for image quality assessment of dental cone beam computed tomography unit.

A standardised testing protocol for evaluation of a wide range of dental cone beam computed tomography (CBCT) performance and image quality (IQ) parameters is still limited and commercially available testing tool is unaffordable by some centres. This study aims to assess the performance of a low-cost fabricated phantom for image quality assessment (IQA) of digital CBCT unit. A customised polymethyl methacrylate (PMMA) cylindrical phantom was developed for performance evaluation of Planmeca ProMax 3D Mid digital dental CBCT unit. The fabricated phantom consists of four different layers for testing specific IQ parameters such as CT number accuracy and uniformity, noise and CT number linearity. The phantom was scanned using common scanning protocols in clinical routine (90.0 kV, 8.0 mA and 13.6 s). In region-of-interest (ROI) analysis, the mean CT numbers (in Hounsfield unit, HU) and noise for water and air were determined and compared with the reference values (0 HU for water and -1000 HU for air). For linearity test, the correlation between the measured HU of different inserts with their density was studied. The average CT number were -994.1 HU and -2.4 HU, for air and water, respectively and the differences were within the recommended acceptable limit. The linearity test showed a strong positive correlation (R2 = 0.9693) between the measured HU and their densities. The fabricated IQ phantom serves as a simple and affordable testing tool for digital dental CBCT imaging.

DeAPIR: Efficient and Enhanced Dental Arch-Guided Panoramic Image Reconstruction from CBCT Data

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT’s three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient’s dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization.

Comparison of the Effective Radiation Dose in the Region of the Facial Skull Between Multidetector CT, Dental Conebeam CT and Intraoperative 3D C-Arms

Study Design Experimental single-centre study of X-ray absorption using a phantom skull. Objective This experimental study aimed to compare the radiation doses of different 3D imaging devices used in maxillofacial surgery, including one Multidetector CT (MDCT), two Conebeam CT (CBCT) and four intraoperative 3D C-arms. Methods Thermoluminescent dosimeters (TLD) were used to determine the absorbed radiation in an Alderson-Rando phantom skull. The phantom skull was positioned in the before mentioned seven devices and a defined 3D facial skull image was acquired. Subsequently, the TLD’S were read out and the effective doses (ED) and the organ doses (OD) were calculated and compared. Results OD varied significantly between tissues as well as between the 3D X-ray devices. The OD of the 3D C-arms were significantly lower than those of all other devices. The OD of the CT, especially in the standard setting, was the highest. Only by special adjustments of the scan protocol regarding CMF requirements for traumatology, the MDCT could achieve almost equivalent doses as the two tested CBCT-scanners. The calculated effective doses were also lowest for the 3D C-arm devices (11.2 to 129.9 μSv). The ED of the MDCT were significant higher (284.52–844.97 μSv) than in all other devices. The ED of the CBCTs (173.7–184.9) were lower than for MDCT but still higher than those of the 3D C-arms. Conclusions Intraoperative imaging using 3D C-arm devices is an effective method to verify reduction results in maxillofacial surgery intraoperatively with significantly lower ED than postoperatively CBCT and MDCT imaging.

The usage of dental cone-beam computed tomography during the COVID-19 pandemic (from 2020 to 2022): A survey of a regional hospital in the northern Taiwan

Background/purposeIn Taiwan, cone-beam computed tomography (CBCT) has already widely used in dentistry. This study explored preliminarily the usage of dental CBCT during the COVID-19 pandemic (from 2020 to 2022) through a survey of a regional hospital in the northern Taiwan. Materials and methodsThis study used purposeful sampling to select a regional hospital in the northern Taiwan to survey its usage of dental CBCT during the COVID-19 pandemic. ResultsIn the surveyed hospital, the number of patients’ visits for the usage of dental CBCT increased from 355 in 2020 to 449 in 2021 and further to 488 in 2022 with a growth rate of 37.46 %, while the growth rates compared to the previous year were 26.48 % in 2021 and 8.69 % in 2022, respectively. There were a total of 1292 patients’ visits for the dental CBCT. The ages of the 1292 patients (573 males and 719 females) ranged from 4 to 89 years. The 50-59-year age group had the highest number of patients’ visits (371, 28.72 %), followed in a descending order by the 60-69-year (293, 22.68 %) and 40-49-year (206, 15.94 %) age groups. The dental CBCT was used mainly for the assessment of dental implants, accounting for 1148 (78.85 %) of the total 1456 irradiations. ConclusionDuring the COVID-19 pandemic, the medical services for dental care and treatments in Taiwan are still maintained normally, and the dental CBCT is also used widely and popularly by the dental patients of all ages, various dental procedures, and various dental specialties.

Diagnostic Study of Mandibular Cortical Index Classification Using Dental Cone-Beam Computed Tomography Findings: A Preliminary Cross-Sectional Study

The prevalence of osteoporosis is high, reportedly affecting 200 million people worldwide. A major problem associated with osteoporosis is that femoral fractures cause a decline in general function and loss of independence, greatly reducing patients’ quality of life. Notably, osteoporosis is an asymptomatic chronic metabolic disease, and its detection is thus often delayed. Interestingly, mandibular cortical index (MCI) classification using dental panoramic radiography is reportedly useful for early detection of osteoporosis. However, this visual classification method can lead to differences in diagnoses among surgeons. The aim of this preliminary study was to analyze cone-beam computed tomography (CBCT) data and examine an objective MCI classification using the findings obtained. MCI classification (classified as C1, C2, or C3) was performed by three examiners on 70 women (91 sites) aged ≥20 years. The mandibular cortical width of all sites was measured using CBCT images. The results showed that the mandibular cortical width was not particularly correlated with age or number of present teeth, and no significant quantitative differences were found between C1 and C2. However, coronal CBCT images of C2 revealed multiple characteristic trabecular bone structures. These structures may be an important finding affecting the classification of two-dimensional dental panoramic radiography images.

CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation

In view of the limitations of current deep learning models in segmenting dental cone-beam computed tomography (CBCT) images, specifically dealing with complex root morphological features, fuzzy boundaries between tooth roots and alveolar bone, and the need for costly annotation of dental CBCT images. We collected dental CBCT data from 200 patients and annotated 45 of them for network training, and proposed a CNN-Transformer Architecture UNet network, which combines the advantages of CNN and Transformer. The CNN component effectively extracts local features, while the Transformer captures long-range dependencies. Multiple spatial attention modules were included to enhance the network’s ability to extract and represent spatial information. Additionally, we introduced a novel Masked image modeling method to pre-train the CNN and Transformer modules simultaneously, mitigating limitations due to a smaller amount of labeled training data. Experimental results demonstrate that the proposed method achieved superior performance (DSC of 87.12%, IoU of 78.90%, HD95 of 0.525 mm, ASSD of 0.199 mm), and provides a more efficient and effective approach to automatically and accurately segment dental CBCT images, has real-world applicability in orthodontics and dental implants.

Development and validation of a 3D anthropomorphic phantom for dental CBCT imaging research.

Optimization of dental cone beam computed tomography (CBCT) imaging is still in a preliminary stage and should be addressed using task-based methods. Dedicated models containing relevant clinical tasks for image quality studies have yet to be developed. To present a methodology to develop and validate a virtual adult anthropomorphic voxel phantom for use in task-based image quality optimization studies in dental CBCT imaging research, focusing on root fracture (RF) detection tasks in the presence of metal artefacts. The phantom was developed from a CBCT scan with an isotropic voxel size of 0.2mm, from which the main dental structures, mandible and maxilla were segmented. The missing large anatomical structures, including the spine, skull and remaining soft tissues, were segmented from a lower resolution full skull scan. Anatomical abnormalities were absent in the areas of interest. Fine detailed dental structures, that could not be segmented due to the limited resolution and noise in the clinical data, were modelled using a-priori anatomical knowledge. Model resolution of the teeth was therefore increased to 0.05mm. Models of RFs as well as dental restorations to create the artefacts, were developed, and could be inserted in the phantom in any desired configuration. Simulated CBCT images of the models were generated using a newly developed multi-resolution simulation framework that incorporated the geometry, beam quality, noise and spatial resolution characteristics of a real dental CBCT scanner. Ray-tracing and Monte Carlo techniques were used to create the projection images, which were reconstructed using the classical FDK algorithm. Validation of the models was assessed by measurements of different tooth lengths, the pulp volume and the mandible, and comparison with reference values. Additionally, the simulated images were used in a reader study in which two oral radiologists had to score the realism level of the model's normal anatomy, as well as the modelled RFs and restorations. A model of an adult head, as well as models of RFs and different types of dental restorations were created. Anatomical measurements were consistent with ranges reported in literature. For the tooth length measurements, the deviations from the mean reference values were less than 20%. In 77% of all the measurements, the deviations were within 10.1%. The pulp volumes, and mandible measurements were within one standard deviation of the reference values. Regarding the normal anatomy, both readers considered the realism level of the dental structures to be good. Background structures received a lower realism score due to the lack of detailed enough trabecular bone structure, which was expected but not the focus of this study. All modelled RFs were scored at least adequate by at least one of the readers, both in appearance and position. The realism level of the modelled restorations was considered to be good. A methodology was proposed to develop and validate an anthropomorphic voxel phantom for image quality optimization studies in dental CBCT imaging, with a main focus on RF detection tasks. The methodology can be extended further to create more models representative of the clinical population.

Monte Carlo simulations of organ and effective doses and dose-length product for dental cone-beam CT.

The use of dental cone-beam CT (CBCT) has increased in recent years. We aimed to calculate the organ and effective doses in dental CBCT using Monte Carlo simulation (MCS) and to correlate the effective dose with the dose-length product (DLP), which is a radiation dose index. Organ and effective doses were calculated by MCS using the adult male and female reference phantoms of the International Commission on Radiological Protection publication 110 in a half-rotation scan of the CBCT scanner Veraviewepocs 3Df. The simulations were performed by setting nine protocols in combination with the field-of-view (FOV) and imaging region. In addition, DLPs were calculated by MCS using the virtual CT Dose Index (CTDI) and CBCT phantoms, with the same protocol. The effective doses were 55 and 195 μSv at the minimum FOV of Φ40 × H40 mm and maximum FOV of Φ 80 × H80 mm, respectively. The organs with the major contribution to the effective dose were the red bone marrow (11.0‒12.8%), thyroid gland (4.0‒12.7%), salivary gland (21.8‒33.2%), and remaining tissues (35.1‒45.7%). Positive correlations were obtained between the effective dose and calculated DLP using the CTDI and CBCT phantoms. Organ and effective doses for each protocol of dental CBCT could be estimated using MCS. There was a positive correlation between the effective dose and DLP, suggesting that DLP can be used to estimate the effective dose of CBCT.

A new dental CBCT metal artifact reduction method based on a dual-domain processing framework

Objective. Cone beam computed tomography (CBCT) has been wildly used in clinical treatment of dental diseases. However, patients often have metallic implants in mouth, which will lead to severe metal artifacts in the reconstructed images. To reduce metal artifacts in dental CBCT images, which have a larger amount of data and a limited field of view compared to computed tomography images, a new dental CBCT metal artifact reduction method based on a projection correction and a convolutional neural network (CNN) based image post-processing model is proposed in this paper. Approach. The proposed method consists of three stages: (1) volume reconstruction and metal segmentation in the image domain, using the forward projection to get the metal masks in the projection domain; (2) linear interpolation in the projection domain and reconstruction to build a linear interpolation (LI) corrected volume; (3) take the LI corrected volume as prior and perform the prior based beam hardening correction in the projection domain, and (4) combine the constructed projection corrected volume and LI-volume slice-by-slice in the image domain by two concatenated U-Net based models (CNN1 and CNN2). Simulated and clinical dental CBCT cases are used to evaluate the proposed method. The normalized root means square difference (NRMSD) and the structural similarity index (SSIM) are used for the quantitative evaluation of the method. Main results. The proposed method outperforms the frequency domain fusion method (FS-MAR) and a state-of-art CNN based method on the simulated dataset and yields the best NRMSD and SSIM of 4.0196 and 0.9924, respectively. Visual results on both simulated and clinical images also illustrate that the proposed method can effectively reduce metal artifacts. Significance. This study demonstrated that the proposed dual-domain processing framework is suitable for metal artifact reduction in dental CBCT images.

Accurate mandibular canal segmentation of dental CBCT using a two-stage 3D-UNet based segmentation framework

ObjectivesThe objective of this study is to develop a deep learning (DL) model for fast and accurate mandibular canal (MC) segmentation on cone beam computed tomography (CBCT).MethodsA total of 220 CBCT scans from dentate subjects needing oral surgery were used in this study. The segmentation ground truth is annotated and reviewed by two senior dentists. All patients were randomly splitted into a training dataset (n = 132), a validation dataset (n = 44) and a test dataset (n = 44). We proposed a two-stage 3D-UNet based segmentation framework for automated MC segmentation on CBCT. The Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD) were used as the evaluation metrics for the segmentation model.ResultsThe two-stage 3D-UNet model successfully segmented the MC on CBCT images. In the test dataset, the mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379.ConclusionsThis automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.

Assessment of Scattered Dose to the Eye in Dentistry: A Systematic Review.

Cone-beam computed tomography (CBCT) is a tool for dental imaging of impactions, maxillofacial discrepancies, facial trauma, and tumors. In addition, It is used in treatment planning for dental implants, orthognathic surgery, and general maxillofacial surgery. There are no standardized methods for utilizing CBCT dosimetry, and there is no consensus among dental and medical physics health professionals regarding dental CBCT imaging procedures. The eyes and thyroid glands are radiosensitive organs that lie outside the primary beam but receive a significant amount of radiation due to scattered radiation. This study aimed to assess the dose to eye lens in patients imaged using CBCT. This review aims to evaluate the scattered doses to the eye from CBCT among adult patients seeking dental treatment. The search included published articles in the Web of Science, PubMed (MeSH and Web PubMed), Medline, and Google Scholar databases using the appropriate keywords from January 2010 to July 2022. The inclusion criteria were based on the method of dose measurement (phantom studies using Optically stimulated luminescence (OSL) and Thermoluminescent dosimeter (TLD), language, and type of protocol used. A literature search was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flow chart. Out of 653 articles identified, 5 met the inclusion criteria. The results show that the scattered radiation dose ranged between 0.103 mSv and 8.3 mSv. This variation exists due to the difference in the field of vision (FOV), phantom exposure, dosimeters used, degree of rotation in the protocol, and finally, the scanner used. The scattered dose to the eye from CBCT is higher than the background radiation, with huge variability in the range of the dose measured. Clear guidelines for utilizing CBCT should be implemented, and dose reference levels should be established for benchmarking and optimization in practice.

Comparison of absorbed doses and organ doses measured with thermoluminescent dosimeters and Gafchromic film for cone beam computed tomography examination of the posterior mandibular region in a head phantom

We aimed to map the correlation between thermoluminescent dosimeters (TLDs) and Gafchromic film for measuring absorbed doses and to compare minimum, maximum, and mean absorbed doses over larger regions of interest and at various craniofacial organs and tissues during cone beam computed tomography (CBCT) exposure of the mandibular third molar region. We positioned TLDs at 75 measurement points in a head phantom. Gafchromic film was cut to the same shape as the 5 levels of the phantom and was placed on top of the TLDs. Both dosimetry methods thus included the surface of each level simultaneously. CBCT scans were made using a 5×5 cm field of view and a rotation angle of 200°. Measurements included absorbed dose distributions, doses at all 75 points, and minimum, maximum, and mean doses within organs and tissues. The correlation of point-dose measurements at all TLD sites with doses measured on film was strong (R2=0.9687), with greatest correlation at lower doses (<2 mGy). Large deviations between TLD and film measurements of minimum and maximum doses and absorbed doses to the organs occurred at all 5 levels. TLD positioning failed to cover several organ sites; for these, only absorbed dose measurements from the film were available. TLDs were unable to sample dose distributions and gradients accurately. The characteristics of Gafchromic LD-V1 film make it a favorable alternative in dental CBCT dosimetry.

Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography

Abstract This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations of existing methods in the accurate restoration of the morphological structures of teeth using dental CBCT data severely damaged by metal implants. We further discuss the usefulness of panoramic images generated from CBCT data for accurate tooth segmentation. We also discuss the possibility of utilizing radiation-free intra-oral scan data as prior information in CBCT image reconstruction to compensate for the damage to data caused by metal implants.

The Application of Deep Learning on CBCT in Dentistry.

Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.