Impact of Image Filters and Observations Parameters in CBCT for Identification of Mandibular Osteolytic Lesions

Do voxel size and filter application on CBCT images improve the diagnosis of condylar morphological alterations? An ex vivo study

BACKGROUND

There is no established protocol for the low doses of cone-beam computed tomography (CBCT) acquisition and filter application on the diagnosis of condylar morphological alterations.

OBJECTIVES

The objective of the study was to evaluate the influence of voxel size and filter application on the diagnosis of condylar morphological alterations in CBCT using an ex vivo model.

METHODS

CBCT scans of 36 temporomandibular joints were acquired using OP300 Maxio with voxel sizes of 0.085, 0.125 and 0.280 mm. Three radiologists evaluated the condyles in the CBCT volumes under three filter modes: no filter, '1x' sharpen filter and '2x' sharpen filter. The area under the ROC curve, sensitivity and specificity were calculated by comparing the evaluators' responses with the reference standard and compared among experimental groups using analysis of variance (ANOVA) (α = 5%).

RESULTS

The area under the ROC curve, sensitivity and specificity were not affected by voxel size and filter application (p > .05). For osteophyte and flattening, there were more true-positive responses in smaller voxel size. For erosion, the increase of true- and false-positive responses occurred with a larger voxel size. Overall, there was a tendency to decrease true positives for osteophyte and erosion and increase false positives for flattening with the '2x' sharpen filter.

CONCLUSIONS

The diagnosis of condylar morphological alterations assessed in this ex vivo analysis is not influenced by the voxel size and the application of the filters. However, attention is needed with the occurrence of false-positive diagnosis with a larger voxel size for erosion and '2x' sharpen filter for flattening.

Influence of enhancement filters in apical bone loss measurement: A cone-beam computed tomography study

Background

The use of cone-beam computed tomography images (CBCT) providing a better assessment of bone injuries, although the sensibility of lesions measurement might be improved by the use of enhancement filters. Objective: This study aimed to analyze the influence of enhancement filters in apical bone loss measurement.

Material and Methods

Eighteen CBCT cases randomly selected of apical bone loss were evaluated. The analyses were carried out following the evaluation in axial, coronal and sagittal protocols, using enhancement filters as Hard, Normal, and Very Sharp. The variables were statistically analyzed by Friedman and Wilcoxon test, Spearman’s rho, and intraclass correlation coefficient.

Results

The differences between filters in axial and sagittal protocols were significant (p<0.05); however, this was not observed in the coronal slice. The use of Hard filter demonstrates better results than Very Sharp and Normal filter, improving significantly the bone loss measurement. A strong, significant and positive correlation was noted for all filters (with p< 0.001), such as a strong agreement between the variables, when the Normal filter was used as a reference.

Conclusions

The use of enhancement filters increases the sensitivity of alveolar bone loss measurement, with relative advantage for Hard filter.

Key words:Cone-Beam computed tomography. endodontics. periapical periodontitis. image enhancement. alveolar bone loss.

Segmentation of facial bone surfaces by patch growing from cone beam CT volumes

OBJECTIVES

The motivation behind this work was to design an automatic algorithm capable of segmenting the exterior of the dental and facial bones including the mandible, teeth, maxilla and zygomatic bone with an open surface (a surface with a boundary) from CBCT images for the anatomy-based reconstruction of radiographs. Such an algorithm would provide speed, consistency and improved image quality for clinical workflows, for example, in planning of implants.

METHODS

We used CBCT images from two studies: first to develop (n = 19) and then to test (n = 30) a segmentation pipeline. The pipeline operates by parameterizing the topology and shape of the target, searching for potential points on the facial bone-soft tissue edge, reconstructing a triangular mesh by growing patches on from the edge points with good contrast and regularizing the result with a surface polynomial. This process is repeated for convergence.

RESULTS

The output of the algorithm was benchmarked against a hand-drawn reference and reached a 0.50 ± 1.0-mm average and 1.1-mm root mean squares error in Euclidean distance from the reference to our automatically segmented surface. These results were achieved with images affected by inhomogeneity, noise and metal artefacts that are typical for dental CBCT.

CONCLUSIONS

Previously, this level of accuracy and precision in dental CBCT has been reported in segmenting only the mandible, a much easier target. The segmentation results were consistent throughout the data set and the pipeline was found fast enough (<1-min average computation time) to be considered for clinical use.