Purpose Anatomical variations of the external nasal wall are highly important, since they play a role in obstruction or drainage of the ostiomeatal complex and ventilation and can consequently elevate the risk of pathological sinus conditions. This study aimed to assess anatomical variations of the nasal cavity and ethmoidal sinuses and their correlations on cone-beam computed tomography (CBCT) scans. Materials and Methods This cross-sectional study evaluated CBCT scans of 250 patients, including 107 males and 143 females, to determine the prevalence of anatomical variations of the nasal cavity and ethmoidal sinuses. All images were taken using a New Tom 3G scanner. Data were analyzed using the chi-square test, Kruskal-Wallis test, and the Mann-Whitney test. Results The most common anatomical variations were found to be nasal septal deviation (90.4%), agger nasi air cell (53.6%), superior orbital cell (47.6%), pneumatized nasal septum (40%), and Onodi air cell (37.2%). Correlations were found between nasal septal deviation and the presence of a pneumatized nasal septum, nasal spur, and Haller cell. No significant associations were noted between the age or sex of patients and the presence of anatomical variations ( P >0.05). Conclusion Radiologists and surgeons must pay close attention to the anatomical variations of the sinonasal region in the preoperative assessment to prevent perioperative complications.

Background. Three-dimensional (3D) cone-beam computed tomography (CBCT) scans play an important role in dental diagnostics and treatment planning, especially in detecting vertical root fractures (VRFs). However, artifacts caused by high-density dental materials can negatively affect the quality of CBCT images by decreasing contrast and masking structures. Objectives. The aim of this study was to assess the efficacy of artifact removal software in detecting VRFs in endodontically treated teeth on CBCT scans. Material and methods. This study evaluated 70 endodontically treated single-rooted teeth. Half of the teeth were cracked by introducing a wedge into the canal and tapping gently with a hammer; the rest remained untouched as a control group. The teeth were then mounted in a bovine rib bone. Soft tissue was simulated using red dental wax. Cone-beam computed tomography scans were taken using the NewTom ® 3G, ProMax ® 3D and Cranex ® 3D CBCT systems, and the MATLAB software was applied. The images were evaluated by 2 oral and maxillofacial radiologists, and the results were recorded in a checklist. The data was analyzed using the κ coefficient, McNemar's test and the receiver operating characteristic (ROC) curves. Results. A significant inter-observer agreement was noted between the 2 observers in detecting VRFs using all CBCT systems. In all systems, the use of the MATLAB software improved the detection of VRFs, but the difference was not significant in the NewTom 3G (p = 0.119) and ProMax 3D (p = 0.455) systems. However, the difference was significant in the Cranex 3D system (p = 0.039). Conclusions. The MATLAB artifact removal software can enhance the detection of VRFs on CBCT scans to some extent.

Background. Bitewing radiography is an important modality useful for the evaluation of teeth in patients of various ages and in different stages of tooth eruption. Clinical examination of proximal surfaces for caries may result in false negative results, especially in tight contact areas. Thus, radiography, as an adjunct to clinical examination, is used as a routine diagnostic modality for caries detection.

Background: Metal artifacts are the major weak points of cone-beam computed tomography (CBCT) scans. This study aimed to quantify the amount of metal artifacts generated by dental implants placed in different anatomical locations in the mandible on CBCT scans. Methods: In this study, 98 CBCT scans of mandibular dental implants with prosthetic crowns were randomly selected irrespective of the age and gender of the patients. Of all 98 implants, 42 were placed in the anterior mandible and 56 were placed in the posterior mandible. The samples were divided into two groups of single and multiple implants. The CBCT scans of each implant were evaluated in apical and cervical cross-sections. The amount of metal artifacts generated around the implants was calculated. Data were analyzed using the Mann-Whitney U test at 0.05 level of significance. Results: Higher amounts of artifacts were noted in the anterior mandible compared to the posterior mandible. Additionally, the amount of artifacts was higher in the cervical cross-section than in the apical cross-section. The difference in the amount of artifacts generated in the cervical cross-section was significant between single and multiple implants (P<0.05). However, this difference was not significant in the apical cross-section (P>0.05). Conclusions: Dental implants always generate metal artifacts on CBCT scans, and the amount of generated artifacts is influenced by the anatomical location of implants in the mandibular arch.