Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data

Panoramic dental tomosynthesis imaging by use of CBCT projection data

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.

Automatic Pulp and Teeth Three-Dimensional Modeling of Single and Multi-Rooted Teeth Based on Cone-Beam Computed Tomography Imaging: A Promising Approach With Clinical and Therapeutic Outcomes

Background Cone-beam computed tomography (CBCT) imaging offers high-quality three-dimensional (3D) acquisition with great spatial resolution, given by the use of isometric voxels, when compared with conventional computed tomography (CT). The current literature supports a median reduction of 76% (up to 85% reduction) of patients' radiation exposure when imaged by CBCT versus CT. Clinical applications of CBCT imaging can benefit both medical and dental professions. Because these images are digital, the use of algorithms can facilitate the diagnosis of pathologies and the management of patients. There is pertinence to developing rapid and efficient segmentation of teeth from facial volumes acquired with CBCT. Methodology In this paper, a segmentation algorithm using heuristics based on pulp and teeth anatomy as a pre-personalized model is proposed for both single and multi-rooted teeth. Results A quantitative analysis was performed by comparing the results of the algorithm to a gold standard obtained from manual segmentation using the Dice index, average surface distance (ASD), and Mahalanobis distance (MHD) metrics. Qualitative analysis was also performed between the algorithm and the gold standard of 78 teeth. The Dice index average for all pulp segmentation (n = 78) was 83.82% (SD = 6.54%). ASD for all pulp segmentation (n = 78) was 0.21 mm (SD = 0.34 mm). Pulp segmentation compared with MHD averages was 0.19 mm (SD = 0.21 mm). The results of teeth segmentation metrics were similar to pulp segmentation metrics. For the total teeth (n = 78) included in this study, the Dice index average was 92% (SD = 13.10%), ASD was low at 0.19 mm (SD = 0.15 mm), and MHD was 0.11 mm (SD = 0.09 mm). Despite good quantitative results, the qualitative analysis yielded fair results due to large categories. When compared with existing automatic segmentation methods, our approach enables an effective segmentation for both pulp and teeth. Conclusions Our proposed algorithm for pulp and teeth segmentation yields results that are comparable to those obtained by the state-of-the-art methods in both quantitative and qualitative analysis, thus offering interesting perspectives in many clinical fields of dentistry.

Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging

BACKGROUND

Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses.

OBJECTIVE

This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging.

METHODS AND MATERIALS

This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy.

RESULTS

Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors.

CONCLUSIONS

This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography

Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient’s mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast.

Detection and classification of mandibular fracture on CT scan using deep convolutional neural network

OBJECTIVES

This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT).

MATERIALS AND METHODS

Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively. The diagnostic performance of the algorithm was compared with the ground truth and evaluated by DICE, accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

RESULTS

One thousand five hundred six mandibular fractures in nine subregions of 686 patients were diagnosed. The DICE of mandible segmentation using U-Net was 0.943. The accuracies of nine subregions were all above 90%, with a mean AUC of 0.956.

CONCLUSIONS

CNNs showed comparable reliability and accuracy in detecting and classifying mandibular fractures on CT.

CLINICAL RELEVANCE

The algorithm for automatic detection and classification of mandibular fractures will help improve diagnostic efficiency and provide expertise to areas with lower medical levels.

Inferior alveolar canal segmentation based on cone-beam computed tomography

PURPOSE

The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone-beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images.

METHODS

In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi-plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K-means clustering algorithm is used to cluster the texture parameters of the gray level-gradient co-occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line-tracking method, and smoothed by the fourth-order polynomial to obtain the final segmentation result.

RESULTS

Twenty-one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively.

CONCLUSIONS

The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented.

The influence of image display systems on observers' preference for visualizing subtle dental radiographic abnormalities

OBJECTIVE

The objectives of this study were to assess observers' preference for standard screens (SSs) or medical displays (MDs) in visualizing difficult-to-diagnose radiographic dental abnormalities and their preference for dental filter tools when utilized with MD systems.

STUDY DESIGN

A retrospective data set of 60 in vivo radiographs consisting of intraoral (n = 20), panoramic (n = 20), and cone beam computed tomography (n = 20) images was created. Three image display monitors, including an SS, an MD, and an MD with 3 dental filter configurations (bone-low density enhancement filter, tooth-high density enhancement filter, and a combined filter representing regular MD), were utilized to assess 4 observers' monitor preferences in detecting radiographically subtle dental abnormalities. The data were analyzed by using binomial distribution. A P value ≤.05 was considered statistically significant.

RESULTS

Although observers expressed preference for MD for visualizing some abnormalities when examining intraoral and panoramic radiographs, MD was not preferred for detection of any abnormalities with cone beam computed tomography. There were no significant differences in preference for SS or MD overall (P ≥ .2024). Observers expressed significant preference for the filters in visualizing all but 2 abnormalities (P ≤ .0252).

CONCLUSIONS

The use of MD monitors enabled with dental filter tools may be preferred for visualizing certain subtle abnormalities.

Reconstruction of Panoramic Dental Images Through Bézier Function Optimization

Computed tomography (CT) and X-ray images have been extensively used as a valuable diagnostic tool in dentistry for surgical planning and treatment. Nowadays, dental cone beam CT has been extensively used in dental clinics. Therefore, it is possible to employ three-dimensional (3D) data from the CT to reconstruct a two-dimensional (2D) panoramic dental image that provides a longitudinal view of the mandibular region of the patient, avoiding an additional exposure to X-ray. In this work, we developed a new automatic method for reconstructing 2D panoramic images of the dental arch based on 3D CT images, using Bézier curves and optimization techniques. The proposed method was applied to five patients, some of them with missing teeth, and smooth panoramic images with good contrast were obtained.

Finite element analysis and experimental evaluation on stress distribution and sensitivity of dental implants to assess optimum length and thread pitch

BACKGROUND AND OBJECTIVE

The dental implant is one of the long term proper remedies to recover a missed tooth as a different prosthetic rehabilitation way. The finite element (FE) method and photoelasticity test are employed to achieve stress distribution and sensitivity in dental implants in order to obtain optimum length and thread pitch.

METHODS

The finite element method and experimental test are developed to evaluate stress distribution and sensitivity around dental implants. Three dimensional FE models of implant-abutment, cortical bone and cancellous bone are created by considering a variation of 0.6 to -1 mm on threads pitch while the implant lengths range from 8.5 mm to 13 mm. Then, axial and oblique forces are applied to the models to obtain the resultant stress contours.

RESULTS

The results indicate that the resultant von Mises stresses in the implant-abutment, cortical bones, and cancellous bones are different. The optimized setting for length and pitch is suggested according to maximum von Mises stress and sensitivity analysis.

CONCLUSIONS

It is concluded that the present FE model accurately predicts stress distribution pattern in dental implants. The results indicate that sensitivity of length play a more significant role in comparison with thread pitch. The accuracy of FEM results in comparison with those of the photoelasticity test recommends applying computation methods in medical practice as great potential in terms of future studies.