Objectives: The purpose of this study was to evaluate how artefacts caused by titanium and zirconia dental implants affect the bone quality assessment in CBCT images. The effect of scan mode and the use of metal artefact reduction (MAR) algorithm on artefacts suppression were taken in consideration.
Methods: Titanium and zirconia dental implants were installed in porcine bone samples and scanned with two CBCT devices with adjustments on scan mode and with the use of MAR. The control group consisted of bone sample without implant and scanned with full-rotation scan mode without MAR. Artefacts extension and bone quality around implants were measured by deviation of grey values and bone histomorphometry measurements (trabecular volume fraction, bone specific surface, trabecular thickness, and trabecular separation), respectively. Mean difference among groups was assessed by within ANOVA with Bonferroni correction. Correlation between bone quality measurements acquired in the experimental and control groups was assessed by Spearman correlation test (α = .05).
Results: No statistical difference was found for artefacts extension in images acquired by half and full-rotation modes (P = .82). The application of MAR reduced artefacts caused by titanium and zirconia dental implants, showing no statistically significant difference from the control group (titanium: P = .20; zirconia: P = .31). However, there was no correlation between bone quality measurements (P < .05).
Conclusions: Bone quality assessment was affected by the presence of artefacts caused by dental implants. Rotation mode did not affect the appearance of artefacts and bone qualitative measurements. MAR was able to decrease artefacts, however, it did not improve the accuracy of bone quality measurements.
Objectives: This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam CT (CBCT).
Methods: CBCT and routine ZTE-MRI data were collected for 30 patients, along with an additional ZTE-MRI obtained with reduced scan time. Scan time-reduced image sets were processed into denoised and AR images based on a deep learning technique. The image quality of the routine sequence, denoised, and AR image sets was compared quantitatively using the signal-to-noise ratio (SNR) and qualitatively using a 3-point grading system (0: poor, 1: good, 2: excellent). The presence of osteoarthritis was assessed in each imaging protocol. Diagnostic accuracy of each protocol was compared against the CBCT results, which served as the reference standard. The SNR and the qualitative scores were compared using analysis of variance test and Kruskal-Wallis test, respectively. The diagnostic accuracy was assessed using Cohen's κ (<0.5 = poor; 0.5 to <0.75 = moderate; 0.75 to <0.9 = good; ≥0.9 = excellent).
Results: Both the denoised and AR protocols resulted in significantly enhanced SNR compared to the routine protocol, with the AR protocol showing a higher SNR than the denoised one. The qualitative assessment also showed highest grade in AR protocol with statistical significance. The osteoarthritis diagnosis showed enhanced agreement with CBCT in denoised (κ = 0.928) and AR images (κ = 0.929) than routine images (κ = 0.707).
Conclusions: A newly developed deep learning technique for both denoising and artefact reduction in ZTE-MRI presented clinical usefulness. Specifically, AR protocol showed significantly improved image quality and comparable diagnostic accuracy comparable to CBCT. It can be expected that this novel technique would help overcome the current limitation of ZTE-MRI for replacing CBCT in bone imaging of TMJ.
Objective: To quantitatively and qualitatively compare directly 2 types of cisternography images for diagnosing trigeminal neuralgia (TN) using 3-T MRI.
Methods: This prospective study recruited 64 patients with a clinical diagnosis or suspicion of TN. Patients were examined through the three-dimensional Constructive Interference in Steady State (CISS) and Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) sequences. Three radiologists quantitatively measured the signal intensity of the trigeminal nerve (cranial nerve V, CN5) (SICN5), cerebrospinal fluid (CSF) (SICSF), and contrast between CN5 and CSF (Cont.). Additionally, 2 radiologists qualitatively evaluated the basilar artery (BA), CN5, CSF, image artefacts, and overall image quality. Statistical analyses included paired-sample t-tests, non-parametric McNemar tests, and the Friedman test (significance set at P < .05).
Results: Mean SICN5 (P < .001), SICSF (P = .679), and Cont. (P < .001) were as follows: 203.08 ± 26.68, 936.03 ± 91, and 3.68 ± 0.74 in CISS; 46.80 ± 16.88, 940.61 ± 71.39, and 23.19 ± 14.52 in SPACE. Low-to-moderate CN5 and BA visibility was observed in all cases in CISS, while it was noted in one case for CN5 and in none for BA in SPACE (P < .001). Homogenous CSF and minor artefacts were observed in 14 cases in CISS, while it was seen in 52 cases for CN5 and 59 for BA in SPACE (P < .001). The overall image quality was scored as 4 in 57 cases in SPACE, while no cases received this score in CISS (P < .001).
Conclusions: Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions provided better images than CISS for evaluating CN5 and prepontine cistern vascularity, indicating a valuable sequence for TN diagnosis.
Advances in knowledge: This study indicates that SPACE should be selected for TN diagnosis instead of CISS sequence.
Objectives: This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in cone-beam CT (CBCT) data to provide a reliable and efficient support tool for dental implant treatment planning.
Methods: A dataset of 90 CBCT scans was annotated as ground truth for mandibular canal segmentation. The dataset was split into training (n = 69), validation (n = 1), and testing (n = 20) subsets. A deep learning model based on a hierarchical convolutional neural network architecture was developed and trained. The model's performance was evaluated using dice similarity coefficient (DSC), 95% Hausdorff distance (HD), and average symmetric surface distance (ASSD). Qualitative assessment was performed by 2 experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at 3 mandibular locations per side.
Results: The trained model achieved a mean DSC of 0.77 ± 0.09, HD of 1.66 ± 0.86 mm, and ASSD of 0.31 ± 0.15 mm on the testing subset. Qualitative assessment showed no significant difference between the deep learning-based segmentation and ground truth in terms of trust and safety across all investigated locations (P > 0.05).
Conclusions: The proposed deep learning-based segmentation technique exhibits sufficient accuracy for the reliable identification of mandibular canals in CBCT scans. This automated approach could streamline the pre-operative planning process for dental implant placement, reducing the risk of neurovascular complications and enhancing patient safety.
Objectives: The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.
Methods: This study utilized a dataset of CBCT volumes collected from 502 individual subjects. Initially, 250 CBCT volumes were used for automatic tooth segmentation and numbering. Subsequently, CBCT volumes from 251 patients diagnosed with periodontal disease were employed to train an AI system to identify various periodontal bone defects using a segmentation method in web-based labelling software. In the third stage, CBCT images from 251 periodontally healthy subjects were combined with images from 251 periodontally diseased subjects to develop an AI model capable of automatically classifying patients as either periodontally healthy or periodontally diseased. Statistical evaluation included receiver operating characteristic curve analysis and confusion matrix model.
Results: The area under the receiver operating characteristic curve (AUC) values for the models developed to segment teeth, total alveolar bone loss, supra-bony defects, infra-bony defects, perio-endo lesions, buccal defects, and furcation defects were 0.9594, 0.8499, 0.5052, 0.5613 (with cropping, AUC: 0.7488), 0.8893, 0.6780 (with cropping, AUC: 0.7592), and 0.6332 (with cropping, AUC: 0.8087), respectively. Additionally, the classification CNN model achieved an accuracy of 80% for healthy individuals and 76% for unhealthy individuals.
Conclusions: This study employed AI models on CBCT images to automatically detect tooth presence, numbering, and various periodontal bone defects, achieving high accuracy and demonstrating potential for enhancing dental diagnostics and patient care.
Objectives: This study was undertaken to generate high-quality radiographic annotations of initial proximal carious lesions based on micro-CT scans. Specifically, we projected manually and automatically acquired annotations of micro-CT scans onto corresponding traditional dental radiographs.
Methods: We utilized the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset of manually annotated initial proximal carious lesions in micro-CT scans and radiographs, the former serving as reference-standard. Production of high-quality radiographic annotations entailed the following: (1) acquiring a reference-standard (for a semi-automated approach) or generating a fully automated micro-CT-based annotation (for a fully automated approach); (2) simulating the corresponding radiograph by projecting the micro-CT scan to find the suitable projection parameters; and (3) superimposing micro-CT-based caries annotations onto radiographs, using identical projection parameters. To evaluate the subsequent accuracy of the annotations on radiograph, we assessed the sensitivity, specificity, and International Caries Classification and Management System (ICCMS) staging of micro-CT-based automated annotations. Projection accuracy was qualitatively gauged.
Results: Micro-CT-based automated annotations outperformed conventional annotations achieving a sensitivity of 50% (95% CI: 42%-59%) compared to 42% (95% CI: 34%-51%) and specificity of 99% (95% CI: 96%-100%) compared to 92% (95% CI: 87%-94%). Among correctly identified micro-CT-based automated annotations, 94% (61/65) were also accurately classified; and 80% of micro-CT projections were ranked as suitably similar to corresponding radiographs.
Conclusions: Micro-CT imaging offers resource-rich depictions, enabling more accurate annotations than those achievable through conventional means. By projecting micro-CT-based annotations of initial proximal caries onto radiographs, some limitations of the conventional radiograph annotation process may be overcome.
Objectives: The lack of consensus regarding the association between airway narrowing and the severity of obstructive sleep apnea (OSA) presents a significant challenge in understanding and diagnosing this sleep disorder. The study aimed to systematically review the literature to investigate the relationship between upper airway measurements and the severity of OSA defined by the apnea-hypopnea index (AHI).
Methods: PubMed, Scopus, and Web of Science were systematically searched on 21 March 2023 for articles on OSA patients as diagnosed by polysomnography, investigating the correlation between upper airway measurements and AHI using cone-beam CT (CBCT) or multidetector CT (MDCT). Quality assessment was done using the Newcastle-Ottawa Scale. The results were subsequently synthesized descriptively.
Results: The database search identified 1253 results. Fourteen studies, encompassing 720 patients, met the eligibility criteria. Upper airway length showed moderate to weak positive correlation with AHI. Minimal cross-sectional area had varying correlations with AHI, ranging from strong negative to no correlation. Nasopharyngeal volumes showed moderate negative to weak correlations with AHI. Total upper airway volume ranged from strong negative to weak correlation with AHI. Other measurements exhibited weak or very weak correlations with AHI.
Conclusions: Among the variables investigated, the minimal cross-sectional area and, to a lesser extent, the volume of the upper airway in OSA patients demonstrated the most promising correlation with the AHI. However, the preponderance of evidence suggests that upper airway length, cross-sectional area and volume as measured by CBCT or MDCT are weak predictors of OSA.
Objectives: This meta-research assessed methodologies used for evaluating peri-implant marginal bone levels on digital periapical radiographs in randomized clinical trials published between 2019 and 2023.
Methods: Articles were searched in four databases. Data on methods for assessing peri-implant marginal bone levels were extracted. Risk of bias assessment was performed.
Results: During full-text reading, 108 out of 162 articles were excluded. Methodological issues accounted for these exclusions, including the absence of radiograph-type information, the lack of radiographic positioners, the missing anatomical references, and the use of panoramic radiographs or tomography. Fifty-four articles were included, most from Europe (70%) and university-based (74%). Radiographic positioners were specified in 54% of articles. Examiner calibration was unreported in 54%, with 69% lacking details. In 59%, no statistical measure assessed examiner agreement. Blinding was unreported or unused in 50%. Marginal bone level changes were the primary outcome of 61%. Most articles (59.3%) raised "some concerns" regarding bias, while 37% showed a high risk of bias, and only two articles (3.7%) demonstrated a low risk of bias.
Conclusions: Several limitations and areas for improvement were identified. Future studies should prioritize protocol registration, standardize radiographic acquisitions, specify examiner details, implement calibration and statistical measures for agreement, introduce blinding protocols, and maintain geometric calibration standards.
Objectives: Due to the increasing use of cone-beam CT (CBCT) in dentistry and considering the effects of radiation on radiosensitive organs, the aim of this study was to investigate the effect of shielding on absorbed dose of eyes, thyroid, and breasts in scans conducted with different parameters using 2 different fields of view (FOV).
Methods: Dose measurements were calculated on a tissue-equivalent female phantom by repeating each scanning parameter 3 times and placing at least 2 thermoluminescent dosimeters (TLD) on each organ, with the averages then taken. The same CBCT scans were performed in 2 different FOV with shielding including thyroid collar, radiation safety glasses, and lead apron and without shielding. The differences between them were analysed statistically. Descriptive statistics and the Wilcoxon test were used for data analysis.
Results: The difference between measurements with and without shielding was statistically significant for all scans (P < .001). The dose reduction associated with the use of shielding ranged from 26.81% to 52.95%. The dose related to the FOV has shown a significant increase, ranging from 8.30% to 623.54%, due to both the variation in the area affected by the primary beam on the organs and changes in the amount of radiation.
Conclusion: There are significant differences in the absorbed dose depending on shielding and FOV usage. Therefore, the CBCT imaging protocol should be optimized by the operator, and special attention should be paid to the use of thyroid collars and radiation safety glasses, considering their effects on image quality.
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