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Abstract Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data. The dataset includes 716 panoramic radiograph data for jaw bone lesions and normal cases collected and labeled by two radiologists from January 2019 to February 2021. Experiments show that guiding network attention with even 5% of attention-labeled data can enhance the diagnostic accuracy for pathology from 92.41 to 96.57%. Furthermore, ablation studies reveal that the proposed augmentation methods outperform prior preprocessing and augmentation combinations, achieving an accuracy of 99.17%. The results affirm the capability of the proposed framework in fine-grained diagnosis using minimally labeled data, offering a practical solution to the challenges of medical image analysis.

Abstract Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging. In this study, we aimed to develop an automatic diagnosis method based on a deep learning model that can determine the true proximity between the mandibular canal and third molars using only panoramic radiographs. A total of 901 third molars shown on panoramic radiographs were examined with CBCT imaging to ascertain whether true proximity existed between the mandibular canal and the third molar by two radiologists (450 molars: true contact, 451 molars: true non-contact). Three deep learning models (RetinaNet, YOLOv3, and EfficientDet) were developed, with performance metrics of accuracy, sensitivity, and specificity. EfficientDet showed the highest performance, with an accuracy of 78.65%, sensitivity of 82.02%, and specificity of 75.28%. The proposed deep learning method can be helpful when clinicians must evaluate the proximity of the mandibular canal and a third molar using only panoramic radiographs without CBCT.

Abstract Background The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging. This study aimed to establish a diagnostic reference for sialadenitis based on the quantitative magnetic resonance imaging (MRI) biomarker IDEAL-IQ and assess its accuracy. Methods Patients with sialadenitis (n = 46) and control subjects (n = 90) that underwent MRI were selected. Considering that the IDEAL-IQ value is a sensitive fat fractional marker to the body mass index (BMI), all subjects were also categorized as under-, normal-, and overweight. The fat fraction of parotid gland in the control and sialadenitis groups were obtained using IDEAL-IQ map. The values from the subjects in the control and sialadenitis groups were compared in each BMI category. For comparison, t-tests and receiver operating characteristic (ROC) curve analyses were performed. Results The IDEAL-IQ fat faction of the control and sialadenitis glands were 38.57% and 23.69%, respectively, and the differences were significant. The values were significantly lower in the sialadenitis group (P), regardless of the BMI types. The area under the ROC curve (AUC) was 0.83 (cut-off value: 28.72) in patients with sialadenitis. The AUC for under-, normal-, and overweight individuals were 0.78, 0.81, and 0.92, respectively. Conclusions The fat fraction marker based on the IDEAL-IQ method was useful as an objective indicator for diagnosing sialadenitis. This marker would aid less-experienced clinicians in diagnosing sialadenitis.

Temporomandibular joint disorders (TMDs) are closely related to the masticatory muscles, but objective and quantitative methods to evaluate muscle are lacking. IDEAL-IQ, a type of chemical shift-encoded magnetic resonance imaging (CSE-MRI), can quantify the fat fraction (FF). The purpose of this study was to develop an MR IDEAL-IQ-based method for quantitative muscle diagnosis in TMD patients. A total of 65 patients who underwent 3 T MRI scans, including CSE-MRI sequences, were retrospectively included. MRI diagnoses and clinical data were reviewed. There were 19 patients in the normal group and 46 patients in the TMD group with unilateral disc displacement. The TMD group was subdivided into those with and without clenching. The right and left FF values of the masseter, medial, and lateral pterygoid muscles were measured twice by two oral radiologists on CSE-MRI, and the average value was used. FF measurements using CSE-MRI showed excellent intra- and inter-observer agreement (ICC > 0.889 for both). There were no statistically significant differences between the right and left FF values in the masseter, medial pterygoid, and lateral pterygoid of the normal group (p > 0.05). A statistically significant difference was found in the TMD group without clenching, in which the masseter muscle had a statistically significantly lower FF value on the disc displacement side (3.94 ± 1.61) than on the normal side (4.52 ± 2.24) (p < 0.05). CSE-MRI, which can reproducibly quantify muscle FF values, is expected to be a biomarker for objective muscle evaluation in TMD patients. The masseter muscle is expected to be particularly useful compared to other masticatory muscles, but further research is needed.

Abstract Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance imaging (MRI), using deep learning and to assess its clinical accuracy. We collected patients who underwent both CBCT and MRI simultaneously in our institution (Seoul). MRI data were registered with CBCT data, and both data were prepared into 512 slices of axial, sagittal, and coronal sections. A deep learning-based synthesis model was trained and the output data were evaluated by comparing the original and synthetic CBCT (syCBCT). According to expert evaluation, syCBCT images showed better performance in terms of artifacts and noise criteria but had poor resolution compared to the original CBCT images. In syCBCT, hard tissue showed better clarity with significantly different MAE and SSIM. This study result would be a basis for replacing CBCT with non-radiation imaging that would be helpful for patients planning to undergo both MRI and CBCT.

Abstract The evaluation of the maxillary sinus is very important in dental practice such as tooth extraction and implantation because of its proximity to the teeth, but it is not easy to evaluate because of the overlapping structures such as the maxilla and the zygoma on panoramic radiographs. When doom-shaped retention pseudocysts are observed in sinus on panoramic radiographs, they are often misdiagnosed as cysts or tumors, and additional computed tomography is performed, resulting in unnecessary radiation exposure and cost. The purpose of this study was to develop a deep learning model that automatically classifies retention pseudocysts in the maxillary sinuses on panoramic radiographs. A total of 426 maxillary sinuses from panoramic radiographs of 213 patients were included in this study. These maxillary sinuses included 86 sinuses with retention pseudocysts, 261 healthy sinuses, and 79 sinuses with cysts or tumors. An EfficientDet model first introduced by Tan for detecting and classifying the maxillary sinuses was developed. The developed model was trained for 200 times on the training and validation datasets (342 sinuses), and the model performance was evaluated in terms of accuracy, sensitivity, and specificity on the test dataset (21 retention pseudocysts, 43 healthy sinuses, and 20 cysts or tumors). The accuracy of the model for classifying retention pseudocysts was 81%, and the model also showed higher accuracy for classifying healthy sinuses and cysts or tumors (98% and 90%, respectively). One of the 21 retention pseudocysts in the test dataset was misdiagnosed as a cyst or tumor. The proposed model for automatically classifying retention pseudocysts in the maxillary sinuses on panoramic radiographs showed excellent diagnostic performance. This model could help clinicians automatically diagnose the maxillary sinuses on panoramic radiographs.

Abstract Quantifying physiological fat tissue in the organs is important to further assess the organ’s pathologic status. This study aimed to investigate the impact of body mass index (BMI), age, and sex on the fat fraction of normal parotid glands. Patients undergoing magnetic resonance imaging (MRI) of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) due to non-salivary gland-related disease were reviewed. Clinical information of individual patients was categorized into groups based on BMI (under/normal/overweight), age (age I/age II/age III), and sex (female/male) and an inter-group comparison of the fat fraction values of both parotid glands was conducted. Overall, in the 626 parotid glands analyzed, the fat fraction of the gland was 35.80%. The mean fat fraction value increased with BMI (30.23%, 35.74%, and 46.61% in the underweight, normal and overweight groups, respectively [p

Abstract This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age range, 3–13 years) with mesiodens were used as a training and validation dataset. Deep learning models based on the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms for detecting mesiodens were developed, and each model was trained 300 times using training (540 images) and validation datasets (60 images). The performance of each model was evaluated based on accuracy, sensitivity, and specificity using 120 test images (60 periapical radiographs with mesiodens and 60 periapical radiographs without mesiodens). The accuracy of the YOLOv3, RetinaNet, and EfficientDet-D3 models was 97.5%, 98.3%, and 99.2%, respectively. The sensitivity was 100% for both the YOLOv3 and RetinaNet models and 98.3% for the EfficientDet-D3 model. The specificity was 100%, 96.7%, and 95.0% for the EfficientDet-D3, RetinaNet, and YOLOv3 models, respectively. The proposed models using three deep learning algorithms to detect mesiodens on periapical radiographs showed good performance. The EfficientDet-D3 model showed the highest accuracy for detecting mesiodens on periapical radiographs.

Abstract The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatment planning. This study proposes a deep learning-based method for fully automatic segmentation of the maxillary sinus, including clear or hazy states, on cone-beam computed tomographic (CBCT) images. A model for segmentation of the maxillary sinuses was developed using U-Net, a convolutional neural network, and a total of 19,350 CBCT images were used from 90 maxillary sinuses (34 clear sinuses, 56 hazy sinuses). Post-processing to eliminate prediction errors of the U-Net segmentation results increased the accuracy. The average prediction results of U-Net were a dice similarity coefficient (DSC) of 0.9090 ± 0.1921 and a Hausdorff distance (HD) of 2.7013 ± 4.6154. After post-processing, the average results improved to a DSC of 0.9099 ± 0.1914 and an HD of 2.1470 ± 2.2790. The proposed deep learning model with post-processing showed good performance for clear and hazy maxillary sinus segmentation. This model has the potential to help dental clinicians with maxillary sinus segmentation, yielding equivalent accuracy in a variety of cases.

Abstract Background As the application of cone-beam computed tomography (CBCT) in head and neck area increases for dental treatment purposes, cosmetic filler materials are incidentally observed. Since the materials are very diverse, unnecessary referrals or additional examination may be performed when clinicians are unfamiliar with the imaging findings. Thus, this study aimed to introduce the imaging characteristics of cosmetic fillers and grafts shown in dental CBCT with dental considerations that the clinician should be aware of. Methods CBCT obtained for dental purpose presenting cosmetic material were selected. The location of the material was identified as buccal, retroantral, parotid space, nose, zygoma, and symphysis. The material was classified as single or multiple, and grouped according to morphology: speckle, round, eggshell, linear, and amorphous. The radiopacity was classified as similar to soft tissue, between soft and hard tissue, similar to hard tissue, and metal. Results Twenty-one patients were reviewed, and all patients were female with mean age of 50.5 years. The buccal space was the most frequent location for multiple filler materials. The symphysis was the next frequent location and only single material were shown in this location. Cases having multiple filler showed diverse shapes while all single materials showed round shape. Fillers showing radiopacity of hard tissue were similar to diseases producing soft tissue calcifications. Metal-density material distributed in spaces induced white and dark streak artifacts in the CBCT image. All single materials presented radiopacity between soft and hard tissue and attached to the bone surface causing mandibular bone resorption. Conclusions Cosmetic materials displayed various imaging features in CBCT acquired during dental procedure. Clinicians should consider that cosmetic material may cause mandibular bone resorption and imaging artifacts on CBCT. Knowledge of the imaging characteristics of cosmetic fillers may help correct diagnosis.

Cone-beam computed tomography (CBCT) can provide 3D images of a targeted area with the advantage of lower dosage than multidetector computed tomography (MDCT; also simply referred to as CT). However, in CBCT, due to the cone-shaped geometry of the X-ray source and the absence of post-patient collimation, the presence of more scattering rays deteriorates the image quality compared with MDCT. CBCT is commonly used in dental clinics, and image artifacts negatively affect the radiology workflow and diagnosis. Studies have attempted to eliminate image artifacts and improve image quality; however, a vast majority of that work sacrificed structural details of the image. The current study presents a novel approach to reduce image artifacts while preserving details and sharpness in the original CBCT image for precise diagnostic purposes. We used MDCT images as reference high-quality images. Pairs of CBCT and MDCT scans were collected retrospectively at a university hospital, followed by co-registration between the CBCT and MDCT images. A contextual loss-optimized multi-planar 2.5D U-Net was proposed. Images corrected using this model were evaluated quantitatively and qualitatively by dental clinicians. The quantitative metrics showed superior quality in output images compared to the original CBCT. In the qualitative evaluation, the generated images presented significantly higher scores for artifacts, noise, resolution, and overall image quality. This proposed novel approach for noise and artifact reduction with sharpness preservation in CBCT suggests the potential of this method for diagnostic imaging.

Abstract This study investigated whether metal artifact reduction (MAR) applied computed tomography (CT) scans could be used to generate precise digital models and explored possible correlations between the amount of metal artifact and model accuracy. Thirty maxillofacial CT scans were randomly selected and a MAR algorithm was applied. By subtracting the original and MAR-applied CT images, the amount of metal artifact was quantified. Digital models were generated from the original and the MAR-applied CT data. Paired digital models were superimposed and shape deviation in planar surface was measured at 10 points in 4 planes. Statistical analyses were performed to compare deviations and to assess correlations between the amount of artifact and deviation. The MAR algorithm reduced metal artifact in all cases. The overall mean deviation of the MAR-applied models was 0.0868 mm, with no significant difference according to the reference plane. The amount of artifact did not significantly influence the accuracy of the digital models. MAR-applied CT is a convenient source for digital modeling with clinically acceptable accuracy. The MAR algorithm can be used regardless of the amount of metal artifact, which are generated by dental prostheses, for the quick and convenient manipulation of dental digital models.

Abstract Background This study aimed to develop evidence-based clinical imaging guidelines to assess the proper implant location following implant surgery and identify potential complications during follow-up. Methods The guideline development process employed an adaptation methodology in accordance with the Korean clinical imaging guidelines (K-CIG). Core (Ovid-Medline, Ovid-Embase, National Guideline Clearinghouse, and Guideline International Network) and domestic databases (KoreaMed, KMbase, and KoMGI) were searched used to retrieve guidelines, and two reviewers analyzed the retrieved articles. The articles were included in this review using well-established inclusion criteria. Results Our online search identified 66 articles, of which 3 were selected for the development of the guidelines. Consequently, based on these three guidelines, we formulated distinct recommendations regarding the appropriate imaging modalities that should be used following implant placement. Conclusions Conventional imaging (e.g., periapical or panoramic radiography) should be the first choice for assessing the implant following its placement and osseointegration. The metal artifacts in Cone Beam Computed Tomography (CBCT) should be considered. However, CBCT is recommended for patients with sensory abnormalities following dental implant surgery to evaluate and identify the underlying cause of implant complications and to determine the appropriate treatment.

Abstract Background The aim of the present study was to evaluate the effectiveness of intraductal irrigation using normal saline in chronic obstructive sialadenitis. Methods Patients who had one of the following symptoms were recruited: pain, swelling, stiffness, and dry mouth. A total of 58 salivary glands in 33 patients were diagnosed as having sialadenitis using sialography and ultrasonography. The patients were divided into two groups (swelling group and dry mouth group), according to the major complaint. Repeated intraductal irrigation was performed on each gland. Difference of symptom severity evaluated using numerical rating scale (NRS), and ductal width measured using ultrasonography were compared between the two groups. Results The average NRS score was significantly decreased from 6.0 to 3.3 after 3–5 visits of intraductal irrigation (P

Abstract Background This study aimed to examine radiologic microarchitectural changes in the mandibles of ovariectomized (OVX) rats through a systematic review and meta-analysis and to identify factors of the OVX rat model that influence on the bone microstructure. Methods Eligible articles were identified by searching electronic databases, including Embase, Medline, Web of Science, and KoreaMed, for articles published from January 1966 to November 2017. Two reviewers independently performed study selection, data extraction, and quality assessment. The pooled standardized mean difference (SMD) with 95% confidence intervals was calculated using a random-effects model. Subgroup analysis and meta-regression were performed to explore the effect of potential sources on the outcomes. The reliability of the results was assessed by sensitivity analysis and publication bias. Results Of 1160 studies, 16 studies (120 OVX and 120 control rats) were included in the meta-analysis. Compared to the control group, the OVX rats’ trabecular bone volume fraction (SMD = − 2.41, P

The prevalence of temporomandibular joint disorder (TMD) is gradually increasing, and magnetic resonance imaging (MRI) is becoming increasingly common as a modality used to diagnose TMD. Edema and osteonecrosis in the bone marrow of the mandibular condyle have been considered to be precursors of osteoarthritis, but these changes are not evaluated accurately and quantitatively on routine MRI. The iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) method, as a cutting-edge MRI technique, can separate fat and water using three asymmetric echo times and the three-point Dixon method. The purpose of this study was to analyze the quantitative fat fraction (FF) in the mandibular condyle head using the IDEAL-IQ method. Seventy-nine people who underwent MRI using IDEAL-IQ were investigated and divided into 1) the control group, without TMD symptoms, and 2) the TMD group, with unilateral temporomandibular joint (TMJ) pain. In both groups, the FF of the condyle head in the TMJ was analyzed by two oral and maxillofacial radiologists. In the TMD group, 29 people underwent cone-beam computed tomography (CBCT) and the presence or absence of bony changes in the condylar head was evaluated. The FF measurements of the condyle head using IDEAL-IQ showed excellent inter-observer and intra-observer agreement. The average FF of the TMD group was significantly lower than that of the control group (p < 0.05). In the TMD group, the average FF values of joints with pain and joints with bony changes were significantly lower than those of joints without pain or bony changes, respectively (p < 0.05). The FF using IDEAL-IQ in the TMJ can be helpful for the quantitative diagnosis of TMD.

Objectives Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. Materials and methods A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis. Results Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference. Conclusion The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

ObjectivesAnatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population.Materials and methodsA total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis.ResultsCluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference.ConclusionThe MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

Abstract This study aims to introduce a new algorithm developed using retrospective cone-beam computed tomography (CBCT) data to obtain a standard dental and mandibular arch shape automatically for an optimal panoramic focal trough. A custom-made program was developed to analyze each arch shape of randomly collected 30 CBCT images. First, volumetric data of the mandible were binarized and projected in the axial direction to obtain 2-dimensional arch images. Second, 30 patients’ mandibular arches were superimposed on the center of the bilateral distal contact points of the mandibular canines to generate an average arch shape. Third, the center and boundary of a panoramic focal trough were obtained using smoothing splines. As a result, the minimum thickness and transition of the focal trough could be obtained. If this new algorithm is applied to big data of retrospective CBCT images, standard focal troughs could be established by race, sex, and age group, which would improve the image quality of dental panoramic radiography.

The variety of cone-beam computed tomography (CBCT) machines and their applications has rapidly increased in recent years, making the dose evaluation of individual devices an important issue. Patient doses from CBCT were assessed with two different methods: optically stimulated luminescence dosimeter (OSLD) measurements and Monte Carlo (MC) simulations, in four different examination modes. Based on an analysis of the measurement process and the obtained values, a recommendation is made regarding which method is more practical and efficient for acquiring the effective dose of CBCT. Twenty-two OSLDs were calibrated and equipped in human phantoms of head and neck organs. They were exposed to radiation from two CBCT units-CS9300 (Carestream Dental LLC, Atlanta, Georgia) and RAYSCAN α+ (Ray Co. Ltd, Hwaseong-si, Korea)-using two different examination modes. The dose recorded using the OSLDs was used to calculate the organ dose and the effective dose for each unit in each examination mode. These values were also calculated using MC software, PCXMC (STUK, Helsinki, Finland). The organ doses and effective doses obtained using both methods were compared for each examination mode of the individual units. The OSLD-measured effective dose value was higher than that obtained using the MC method for each examination mode, except the dual jaw mode of CS9300. The percent difference of the effective dose between the two methods ranged from 4.0% to 14.3%. The dose difference between the methods decreased as the field of view became smaller. The organ dose values varied according to the method, although the overall trend was similar for both methods. The organs showing high doses were mostly consistent for both methods. In this study, the effective dose obtained by OSLD measurements and MC simulations were compared, and both methods were described in detail. As a relatively efficient and easy-to-perform method, we cautiously suggest using MC simulations for dose evaluations in the future.

Recently, the number of patients who visit the hospital with symptoms of temporomandibular joint disorders (TMD) has been gradually increasing, and the need for special imaging such as cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI) is increasing for accurate diagnosis and treatment. The purpose of this study was to help the image guideline by comparing the usefulness of CBCT and MRI according to the clinical symptoms of TMD patients and further examining whether the usefulness varies with age. A total of 473 temporomandibular joints (TMJs) with clinical symptoms of TMD who underwent both CBCT and MRI examinations were retrospectively reviewed. Clinical symptoms included pain, sound and limited mouth opening. The CBCT findings included sclerosis, flattening, erosion and osteophyte, while the MRI findings were defined as disc deformity, disc derangement or joint effusion. Joints were divided according to the presence of CBCT and MRI findings as follows: type I (neither CBCT nor MRI findings), type II (only CBCT findings), type III (only MRI findings) and type IV (both CBCT and MRI findings). We assessed the usefulness of the two imaging modalities by comparing the frequency of those four groups according to clinical symptoms and age. In TMD patients with the clinical symptoms, MRI and CBCT are complementary, but if it is difficult to choose the first of these two modalities, MRI is more recommended, and the younger the patient, the more the MRI is recommended.