Dento maxillo facial radiology最新文献

Objective: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.

Methods: A total of 24 384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height, and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) "Coordinate" mode, which uses the (continuous) XYZ coordinates of the isocentre, and (2) "AP/JAW" mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated, and tested using a 3/1/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).

Results: The mean absolute error (MAE) for organ dose/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in "Coordinate" mode and from 2.74% (red bone marrow) to 14.13% (brain) in "AP/JAW" mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.

Conclusions: NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.

Objectives: To compare a novel photon-counting sensor, 2 CBCT protocols and 2 CMOS sensors on the detection of gaps between a gutta-percha cone and root canal walls.

Methods: Twenty-five mandibular incisors were prepared to 45/0.04 (size/taper) at working length. Teeth were placed in a partially dentate mandible and single gutta-percha cones of 7 sizes were placed at length, one at a time, for image acquisition with a photon-counting sensor, 2 CBCT protocols (90 µm3, 120 µm3) and 2 CMOS sensors. Three calibrated observers assessed images for gap presence. Sensitivity, specificity, accuracy, AUC, and agreement with gold standard were determined using ANOVA and Tukey test (P ≤ .05).

Results: Photon-counting sensor showed superior sensitivity and accuracy (88.47%, 81.57%), significantly higher than the CBCT protocols (50.70%-56.33%, 45.87%-53.17%). Contrarily, the photon-counting sensor showed the lowest specificity (40.27%), significantly lower than the CBCT protocols (90.27%, 97.23%). CMOS sensors showed sensitivity, specificity, and accuracy between 72.23%-74.53%, not differing from other modalities. All intraoral sensors showed AUC around 82.87%-84.03%, significantly higher than CBCT protocol 120 µm3 (74.07%). The file size was inversely related to gap size and percentual agreement with gold standard.

Conclusions: CMOS sensors showed consistent results, while the photon-counting sensor had the highest sensitivity but lacked specificity. CBCT protocols excelled in specificity but had lower sensitivity.

Advances in knowledge: Novel photon-counting sensors and CBCT imaging provided no significant advantage over conventional sensors in assessing gaps as an indicator of quality of root canal filling. Furthermore, smaller gaps were more difficult to detect, regardless of the imaging technique used.

Objectives: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.

Methods: We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net convolutional neural network machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks, including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL.

Results: Over 98% of predicted GM, CEJ, and ABC distances are within 200 µm of the manual annotation. Bland-Altman analysis revealed biases (bias of machine learning vs ground truth) of -0.1 µm, -37.6 µm, and -40.9 µm, with 95% limits of agreement of [-281.3, 281.0] µm, [-203.1, 127.9] µm, and [-297.6, 215.8] µm for iGR, iGH, and iABL, respectively, when compared to manual annotations. On the test dataset, the biases were 167.5 µm, 40.1 µm, and 78.7 µm with 95% CIs of [-1175 to 1510] µm, [-910.3 to 990.4] µm, and [-1954 to 1796] µm for iGR, iGH, and iABL, respectively.

Conclusions: The proposed machine learning model demonstrates robust prediction performance, with the potential to enhance the efficiency of clinical periodontal diagnosis by automating landmark identification and clinical metrics measurements.

Objective: Recently, SPECT/CT has been widely accepted as a valuable diagnostic tool in dentistry. The aim of this study was to investigate submandibular sialolithiasis with CT and SPECT/CT, especially CT values, standardized uptake values (SUVs), and salivary gland excretion in the parotid and submandibular glands.

Methods: A prospective study was performed in 13 patients with submandibular sialolithiasis who underwent CT and salivary gland SPECT/CT. The CT values and the SUVs of parotid and submandibular glands were obtained using a workstation and software. The salivary gland excretion in the parotid and submandibular glands was defined as ratio of pre- to post-stimulation on SUVs. A p value lower than 0.05 was considered as statistically significant.

Results: In the submandibular glands with sialoliths, the average CT values were significantly correlated with the maximum SUVs at ratio of pre-stimulation (r = 0.558, p＜0.05). The maximum SUVs at ratio of pre- to post-stimulation in the submandibular glands with and without sialoliths were 1.5 ± 1.1 and 2.1 ± 0.7, respectively (p = 0.026).

Conclusion: The salivary gland SPECT/CT SUVs can be useful in clinical practice for the quantitative management of parotid and submandibular glands in patients with submandibular sialolithiasis.

Objective: This study aimed to evaluate the effectiveness of deep learning method for denoising and artifact reduction (AR) in zero-TE (ZTE) magnetic resonance imaging (MRI). Also, Clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam computed tomography (CBCT).

Methods: For thirty patients CBCT and routine ZTE-MRI data was collected, and an additional reduced scan time-ZTE-MRI was also obtained. Scan time-reduced image sets were processed into denoised and AR image based on deep learning technique. The image quality of routine sequence, de-noised and AR image sets were compared in quantitative evaluation using signal-to-noise ratio (SNR), and in qualitative using 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 was compared using analysis of variance test and Kruskal-Wallis test, respectively. The diagnostic accuracy was assessed using the Cohen κ (<0.5 = poor; 0.5 to < 0.75 = moderate; 0.75 to < 0.9 = good; ≥0.9 = excellent).

Results: Both denoised and AR protocol resulted the significantly enhanced SNR compared to routine protocol and AR protocol showed higher SNR than 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 artifact reduction in ZTE-MRI presented clinical usefulness. Specifically, AR protocol showed significantly improved image quality and comparable diagnostic accuracy as 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.

Objectives: The purpose of this study was to propose an automatic landmark identification method using curvature to improve the reproducibility of landmark identification and compare its performance with that of a previously established method.

Methods: A total of 30 patients with facial deformities associated with mandibular prognathism were included. Computed tomography (CT) images were utilized to construct three-dimensional (3D) surface models, followed by an analysis of their surface curvature distribution. A statistical shape model (SSM) was created as a deformable mean model to identify the six landmarks. These landmarks were automatically identified in each patient model by registering the SSM in the individual patient models. Two registration methods were employed: the proposed curvature-based and previously established methods. Both methods involved rigid and non-rigid registration processes; however, the proposed method included additional curvature-based registration using a curvature-driven, non-rigid Iterative Closest Point (ICP) algorithm. The Euclidean distances between the manually and automatically identified landmarks were measured and compared between the two methods.

Results: The Euclidean distance was significantly lower in the gonion and right coronoid process when the proposed method was used compared to the previous method. No significant differences were observed in the condylion or left coronoid process.

Conclusions: These findings suggest that the curvature-based registration successfully automates landmark identification on 3D mandibular images, providing higher accuracy in convex regions and improved reproducibility in landmark identification.

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 algorithm (MAR) 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. Control group consisted of bone sample without implant and scanned with full rotation scan mode without MAR. Artefacts extension was measured by deviation of gray values and bone quality around implants was measured by bone histomorphometry measurements (trabecular volume fraction, bone specific surface, trabecular thickness, and trabecular separation). Mean difference among groups was assessed by within ANOVA with Bonferroni correction. Correlation between bone quality measurements acquired in experimental and control groups were assessed by Spearman correlation test (α = 0.05).

Results: No statistical difference was found for artefacts extension in images acquired by half and full-rotation mode (p = 0.82). The application of MAR reduced artefacts caused by titanium and zirconia dental implants, showing no statistically significant difference to the control group (Titanium: p = 0.20; Zirconia: p = 0.31). However, bone quality measurements did not correlate to the control group (p < 0.05).

Conclusions: Bone quality assessment was affected by the presence of artefacts caused by dental implants. Scan mode did not affect the appearance of artefacts and did not affect the bone qualitative measurements. MAR was able to decrease artefacts, however, it did not improve the accuracy of bone quality measurements.

Objectives: This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in CBCT data to provide a reliable and efficient support-tool for dental implant treatment planning.

Methods: A dataset of 90 cone beam computed tomography (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 two experienced dental imaging practitioners who evaluated the segmentation quality in terms of trust and safety on a 5-point Likert scale at three 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 segmentations 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: To screen the existing scientific literature and to evaluate the reliability of skull joints as biological markers for age estimation when analyzed via computed tomography (CT).

Methods: The study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered in Open Science Framework (DOI: 10.17605/OSF.IO/PCVEF). Eligible studies included observational cross-sectional research that assessed skull joints for age estimation through CT. Data from five databases were screened: Medline/PubMed, Scopus, LILACS, SciELO and Open Grey. The risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Tools for Systematic Reviews.

Results: A total of 15 studies out of 4633 initially screened were eligible. The studies tested age estimation based on the spheno-occipital joint (53.33%) and cranial sutures, namely coronal, sagittal and lambdoid (46.66%). Multi-slice CT was the most commonly used imaging modality (66.66%), with a slice thickness of < 1mm in 93.33% of studies. All the studies that assessed the spheno-occipital joint endorsed its application for age estimation, but some (37.5%) stressed limitations. Four (57.14%) out of the seven studies that assessed the cranial sutures raised concerns about their use for age estimation (three advised against it). Most of the studies had a low risk of bias (86.66%).

Conclusion: Findings supported the forensic application of the spheno-occipital joint for age estimation in adolescents and young adults, despite variability in fusion age. Cranial sutures were largely discouraged as sole markers because of unsatisfactory accuracy and high error risks.

Objective: To develop and test a prototype for interproximal radiography positioning intended for pediatric dentistry and compare the technical quality of image receptor-holding devices (IRHD) commonly used in clinical practice.

Methods: Six prototypes, with three wedge dimensions (0.5 mm, 1 mm, and 2 mm) in upper and lower positions on the bitewing surface were compared regarding their capacity to acquire interproximal radiographs without overlapping surfaces with other IRHDs: RinnXCPTM, Hawe KerrTM, and Cone Indicator. Fifteen graduate students obtained images of deciduous molars in a child's skull and mandible. The chi-square and Fisher's exact tests were employed to analyze significant differences in the number of repetitions and failures in obtaining correct images. A one-way ANOVA assessed the difference between the mean times required for image acquisition according to each IRHD, adopting a significance level of 5%.

Results: The effectiveness of the tested devices, evaluated by the number of correct and incorrect acquisitions using Fisher's exact test, revealed a p-value of 0.057. The ANOVA demonstrated statistically significant differences in the mean acquisition times (p < 0.0000). The upper and lower wedge prototypes 2 mm performed better in acquiring only one radiograph (76.7%) and took less time to execute the technique (1.17 min and 1.12 min, respectively).

Conclusion: The prototypes showed comparable performance to alternative IRHDs but provided advantages in time efficiency and radiation exposure.