Imaging Science in Dentistry最新文献

A central odontogenic fibroma is a rare benign tumor composed of mature fibrous connective tissue with variable amounts of odontogenic epithelium. It appears at similar rates in the maxilla and mandible. In the maxilla, it usually occurs anterior to the molars. Radiographically, central odontogenic fibroma commonly presents as a multilocular or unilocular radiolucency with a distinct border. This paper reports a case of an aggressive central odontogenic fibroma involving the right posterior maxilla of a 53-year-old man. Radiographs showed an extensive soft tissue mass involving the entire right maxilla with frank bone resorption. The patient had a history of 2 operations in the region, both more than 2 decades ago. Although it was impossible to confirm the previous diagnoses, it was presumed that this case was a recurrent lesion.

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol.

Materials and methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient.

Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion.

Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

Purpose: The aim of this study was to investigate the prevalence of infraorbital canal protrusion in an Egyptian subpopulation using cone-beam computed tomography and to describe its radiographic representation.

Materials and methods: This retrospective cross-sectional study was conducted using the records of 77 patients and 123 maxillary sinuses. The full lengths of the sinuses were visible for the detection of infraorbital canal protrusion. The infraorbital canals were classified into 3 types based on their relation to the sinus. If the septum was present, its length and its distance from the sinus floor were measured. Qualitative and quantitative variables were described as percentages and means with standard deviations, respectively.

Results: The infraorbital canal most commonly presented as the normal confined type (detected in 78.1% of sinuses), whereas the suspended (or protruded) variant was found in 14.6% of the examined sinuses. The septal length ranged from 0.9 to 5.1 mm, with a mean of 2.8±1.1 mm. The distance to the sinus floor ranged from 5.2 to 29.6 mm depending on the sinus shape and size.

Conclusion: The present study indicates that protrusion of the infraorbital canal is not rare, and surgeons that use the maxillary sinuses as corridors for their procedures must be more cautious, especially in the upper lateral confines of the sinus.

Purpose: The aim of this study was to analyze the relationships between bone density measurements obtained using cone-beam computed tomography (CBCT) and morphometric parameters of bone determined by histomorphometric analysis.

Materials and methods: In this in vivo study, 30 samples from the maxillary bones of 7 sheep were acquired using a trephine. The bone samples were returned to their original sites, and the sheep heads were imaged using CBCT. On the CBCT images, gray values were calculated. In the histomorphometric analysis, the total bone volume, the trabecular bone volume (referred to simply as bone volume), and the trabecular thickness were assessed.

Results: Statistical testing showed significant correlations between CBCT gray values and total bone volume (r=0.537, P=0.002), bone volume (r=0.672, P<0.001), and trabecular thickness (r=0.692, P<0.001), as determined via the histomorphometric analysis.

Conclusion: The results indicate a significant and acceptable association between CBCT gray values and bone volume, suggesting that CBCT may be used in bone densitometry.

Purpose: This study evaluated the prevalence of dental implant positioning errors and the most frequently affected oral regions.

Materials and methods: A sample was obtained of CBCT images of 590 dental implants from 230 individuals who underwent diagnosis at a radiology center using cone-beam computed tomography from 2017 to 2020. The following variables were considered: thread exposure, violation of the minimum distance between 2 adjacent implants and between the implant and tooth, and implant contact with anatomical structures. Descriptive data analysis and the Pearson chi-square test (P<0.05) were performed to compare findings according to mouth regions.

Results: Most (74.4%) of the 590 implants were poorly positioned, with the posterior region of the maxilla being the region most frequently affected by errors. Among the variables analyzed, the most prevalent was thread exposure (54.7%), followed by implant contact with anatomical structures, violation of the recommended distance between 2 implants and violation of the recommended distance between the implant and teeth. Thread exposure was significantly associated with the anterior region of the mandible (P<0.05). The anterior region of the maxilla was associated with violation of the recommended tooth-implant distance (P<0.05) and the recommended distance between 2 adjacent implants (P<0.05). Implant contact with anatomical structures was significantly more likely to occur in the posterior region of the maxilla (P<0.05).

Conclusion: Many implants were poorly positioned in the posterior region of the maxilla. Thread exposure was particularly frequent and was significantly associated with the anterior region of the mandible.

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches.

Materials and methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models.

Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively.

Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.