Integration of digital dental casts in cone-beam computed tomography scans

Create virtual dentoskeletal model by superimposing digital dental cast into cone-beam computed tomography scan

PURPOSE

Many artifacts and obstacles associated with cone-beam computed tomography (CBCT) scan can obscure or distort the details of the teeth and occlusal surface, like distorted teeth, streak artifacts, noise, and some malocclusion cases with excessive overlapping between jaws cause decrease the interocclusal space, which can impact diagnosis and treatment planning, and the 3D reconstruction accuracy. Optimizing dental precision by Integrating CBCT scans with other imaging modalities, supply more information to enhance CBCT accuracy, mainly in dental areas with limited clarity.

METHODS

Performing the Structure-from-Motion (SfM) photogrammetry method, using phone camera and photograph studio setup using simple hardware, to digitize the dental casts and obtain an accurate digital dental model. Using this digital dental model to enhance dental precision in the CBCT data by performing the superimposition process, using a surface-based registration method and integration process to create a virtual dentoskeletal model. Evaluate the accuracy and quality of the superimposition results using qualitative (visual inspection) and quantitative measures.

RESULTS

The differences between the virtual dentoskeletal model and the reference CBCT model are calculating by the 3D Euclidean distance, the mean ± SD are 0.212 ± 0.169 mm and 0.26 ± 0.149 mm for the maxilla and mandible, respectively. The color-coded map shows that the two surfaces are similar, but the extremist values are concentrated in the dental region due to the presence of the noise in the reference model and the gingiva in the virtual dentoskeletal model.

CONCLUSIONS

The resulting virtual dentoskeletal model can be viewed and manipulated on a computer screen, allowing for a detailed analysis of the teeth and supporting structures. The 3D model generated by the SfM photogrammetry technique did well during the superimposition process, representing a reliable method for virtual-based processing such as orthognathic surgery planning and splint design.

Superimposition of virtual models using palatal rugae and maximum habitual intercuspation

INTRODUCTION

The superimposition of 3 dimensions (3D) digital models has been increasingly used for evaluating dental changes resulting from orthodontic treatment, and different superimposition techniques have been described. Although the maxilla has areas with greater stability for superimposition, such as the palatal rugae, there is still no reliable method for superimposing models of the lower arch.

OBJECTIVE

Therefore, this article aims to describe a technique for superimposing virtual models.

METHODS

To evaluate pre- and post-orthodontic treatment changes, the Geomagic Qualify 2013 software (3D Systems®, Rock Hill, South Carolina, USA) was used, with reference points in the maxilla, including the rugae and a reference area in the palate and midpalatal raphe. The lower arch was superimposed using the maximum habitual intercuspation (MHI) model as reference.

RESULTS AND CONCLUSION

3D models superimposition using palatal rugae and MHI occlusion seems to offer satisfactory results in the interpretation of clinical changes at different follow-up moments in terms of development and/or orthodontic treatment.

Accuracy of a surface-based fusion method when integrating digital models and the cone beam computed tomography scans with metal artifacts

The aim of this study was to evaluate the intra- and inter-observer reliability of maxillary digital dental model integration into cone-beam computed tomography (CBCT) scans to reconstruct three-dimensional (3D) skeletodental models for orthognathic patients. This retrospective study consisted of CBCT and digital maxillary dentition images of 20 Class III orthognathic patients. After two repeated fusions of digital cast images with reconstructed CBCT images by a digital engineer and an orthodontist respectively, the 3D coordinate values of the canines, first molars, and central incisors were evaluated. The intra- and inter-observer reliability of 3D positions of maxillary teeth were compared using intraclass correlation coefficients (ICCs). Intra-observer reliability of x-, y-, and z-coordinate values of maxillary teeth showed significant and excellent agreement in an engineer (0.946 ≤ ICC ≤ 1.000) and an orthodontist (0.876 ≤ ICC ≤ 1.000). The inter-observer reliability of the y- and z-coordinates of each tooth was significantly excellent or good, but that of the x-coordinates showed insignificantly poor to moderate agreement. This study showed that the integration of maxillary digital models into CBCT scans was clinically reliable. However, considering the low inter-observer reliability on the x-coordinates of dentition, clinical experience and repeated learning are needed for accurate application of digital skeletodental model in orthognathic patients.

Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment

Objective

This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodontic treatment and to compare the fabrication process of integrated tooth models (ITMs) with manual method.

Material and methods

Intraoral scans and corresponding CBCT scans before and after treatment were obtained from 15 patients who completed orthodontic treatment with premolar extraction. A total of 600 ITMs were generated using deep learning technology and manual methods by merging the intraoral scans and CBCT scans at pretreatment. Posttreatment intraoral scans were integrated into the tooth model, and the resulting estimated root positions were compared with the actual root position at posttreatment CBCT. Discrepancies between the estimated and actual root position including average surface differences, arch widths, inter-root distances, and root axis angles were obtained in both the deep learning and manual method, and these measurements were compared between the two methods.

Results

The average surface differences of estimated and actual ITMs in the manual method were 0.02 mm and 0.03 mm for the maxillary and mandibular arches, respectively. In the deep learning method, the discrepancies were 0.07 mm and 0.08 mm for the maxillary and mandibular arches, respectively. For the measurements of arch widths, inter-root distances, and root axis angles, there were no significant differences between estimated and actual models both in the manual and in the deep learning methods, except for some measurements. Comparing the two methods, only three measurements showed significant differences. The procedure times taken to obtain the measurements were longer in the manual method than in the deep learning method.

Conclusion

Both deep learning and manual methods showed similar accuracy in the integration of intraoral scans and CBCT images. Considering time and efficiency, the deep learning automatic method for ITMs is highly recommended for clinical practice.

Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities

Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite for surgical planning for dental implants or orthognathic surgery. We propose a novel method that performs fully automatic registration between a cone-beam CT image and an optically scanned model. To build a robust and automatic initial registration method, deep pose regression neural networks are applied in a reduced domain (i.e., two-dimensional image). Subsequently, fine registration is performed using optimal clusters. A majority voting system achieves globally optimal transformations while each cluster attempts to optimize local transformation parameters. The coherency of clusters determines their candidacy for the optimal cluster set. The outlying regions in the iso-surface are effectively removed based on the consensus among the optimal clusters. The accuracy of registration is evaluated based on the Euclidean distance of 10 landmarks on a scanned model, which have been annotated by experts in the field. The experiments show that the registration accuracy of the proposed method, measured based on the landmark distance, outperforms the best performing existing method by 33.09%. In addition to achieving high accuracy, our proposed method neither requires human interactions nor priors (e.g., iso-surface extraction). The primary significance of our study is twofold: 1) the employment of lightweight neural networks, which indicates the applicability of neural networks in extracting pose cues that can be easily obtained and 2) the introduction of an optimal cluster-based registration method that can avoid metal artifacts during the matching procedures.

Impact of number of registration points on the positional accuracy of a prosthetic treatment plan incorporated into a cone beam computed tomography scan by surface scan registration: An in vitro study

OBJECTIVES

To evaluate the accuracy of a prosthetic treatment plan incorporation into a cone beam computed tomography (CBCT) scan using point-based registration with three registration points selected and to evaluate the impact of number of registration points on prosthetic plan accuracy.

MATERIAL AND METHODS

A CBCT scan of a completely dentate master model with removable teeth was exposed after removing the mandibular left first premolar, second premolar, and first molar. A digital scan of the master model with all teeth present was made by scanning a stone replica using a laboratory scanner. The digital model was registered onto the three-dimensional (3D) volume rendering of the CBCT scan using implant planning software. The point-based registration was repeated using three, four, five, six, seven, eight, nine, and 10 reference points. Metrology software was used to measure the 3D deviation of the registered models for each reference point group on standard tessellation language (STL) files obtained from the CBCT scans. An STL file of the master model with all teeth present obtained from another CBCT scan was used as reference.

RESULTS

Using three registration points, the registered prosthetic plan had a mean absolute deviation of 17.63 µm from the reference. Increasing the number of registration points failed to demonstrate statistically significant effects on the deviation (p > 0.05).

CONCLUSIONS

For this clinical scenario, three registration points provided adequate accuracy for prosthetic plan incorporation into CBCT scans. Increasing the number of registration points had no significant impact on the prosthetic plan accuracy in this study.

Comparison of postoperative intraoral scan versus cone beam computerised tomography to measure accuracy of guided implant placement-A prospective clinical study

OBJECTIVE

To evaluate the accuracy of implant placement with a digitally planned guided implant procedure. Two methods for identifying the actual postoperative positioning of the implants were compared: CBCT and IO scanning.

MATERIAL AND METHODS

Twenty-eight implants with a sandblasted and acid-etched surface were placed in thirteen patients using tooth-supported surgical guides following a digital planning procedure. The implants were submerged for 12-15 weeks. New CBCT images were taken for identification of the implant position. After second stage surgery, scan bodies were mounted on the implants and scanned with an IO digital scanner. The recordings from the CBCT images and the IO scans were compared with respect to the identified positions of the implants.

RESULTS

The study did not resolve any significant differences of the identified positioning of the implants as measured by CBCT or IO, except for the apical deviations at the coronal and apical points. The angular difference between CBCT and IO scanning at the coronal point was -0.011 (±0.6) degrees, whereas the 3D deviation was 0.03(±0.17) mm. The distal deviation between CBCT and IO scanning was 0.01(± 0.16) mm, and the vestibular deviation 0.033(± 0.16) mm and the apical deviation difference was 0.09(± 0.16) mm. The 3D deviation at the apical point was 0.04(± 0.22) mm. The distal deviation between CBCT and IO scanning was 0.06(± 0.19) mm, and the vestibular deviation 0.032(± 0.23) mm and the apical deviation difference was 0.09(± 0. 16) mm.

CONCLUSION

The study demonstrated that accuracy measurements using IO scanning yields comparable results to those obtained by CBCT.

Integration of digital dental casts in cone beam computed tomography scans-a clinical validation study

OBJECTIVES

Images derived from cone beam computed tomography (CBCT) scans lack detailed information on the dentition and interocclusal relationships needed for proper surgical planning and production of surgical splints. To get a proper representation of the dentition, integration of a digital dental model into the CBCT scan is necessary. The aim of this study was to validate a simplified protocol to integrate digital dental models into CBCT scans using only one scan.

MATERIALS AND METHODS

Conventional protocol A used one combined upper and lower impression and two CBCT scans. The new protocol B included placement of ten markers on the gingiva, one CBCT scan, and two separate impressions of the upper and lower dentition. Twenty consecutive patients, scheduled for mandibular advancement surgery, were included. To validate protocol B, 3-dimensional reconstructions were made, which were compared by calculating the mean intersurface distances obtained with both protocols.

RESULTS

The mean distance for all patients for the upper jaw is 0.39 mm and for the lower jaw is 0.30 mm. For ten out of 20 patients, all distances were less than 1 mm. For the other ten patients, all distances were less than 2 mm.

CONCLUSIONS

Mean distances of 0.39 and 0.30 mm are clinically acceptable and comparable to other studies; therefore, this new protocol is clinically accurate.

CLINICAL RELEVANCE

This new protocol seems to be clinically accurate. It is less time consuming, gives less radiation exposure for the patient, and has a lower risk for positional errors of the impressions compared to other integration protocols.

Registration of cone beam computed tomography data and intraoral surface scans - A prerequisite for guided implant surgery with CAD/CAM drilling guides

OBJECTIVES

Guided implant surgery (GIS) is performed with drilling guides that are produced on the virtual tooth model using CAD/CAM technology. The prerequisite for this workflow is the alignment of patients cone beam computed tomography CBCT and surface scan (registration). Dental restorations may cause deteriorating imaging artifacts in CBCT data, which in turn can have an impact on the registration process. The influence of the user and the preprocessing of data and of image artifacts on the registration accuracy were examined.

MATERIAL AND METHODS

CBCT data and intraoral surface scans of 36 patients were used for virtual implant planning in coDiagnostiX (Dentalwings, Montreal, Canada). CBCT data were reconstructed to a three-dimensional anatomical model with the default settings provided by the software and also manually by four different examiners. Subsequently, the CBCT and intraoral surface models were registered by each examiner with the help of anatomical landmarks. Patients' data were subdivided into four groups (A-D) according to the number of metallic restorations: A = 0-2 restorations, B = 3-5 restorations, C = 6-8 restorations and D > 8 restorations. After registration, the distances between CBCT and dental surface models were measured. Linear regression models were used to assess the influence of the segmentation, the examiner and to the number of restorations (P < 0.05).

RESULTS

The deviations between surface scan and CBCT models accounted to 0.54 mm (mean). The mean deviations were 0.69 mm (max. 24.8 mm) and 0.4 mm (max. 9.1 mm) for default and manual segmentation, respectively. Mean deviations of 0.36 mm (Group A), 0.43 mm (Group B), 0.67 mm (Group C) and 1.01 mm (Group D) were recorded. The segmentation (P = 0.000), the user (P = 0.0052) and the number of restorations (P = 0.0337) had a significant influence on the registration accuracy.

CONCLUSIONS

The deviation between CBCT and surface scan model resulting from inaccurate registration is transferred to the surgical field and results in a deviation between the planned and actual implant position. The registration accuracy in commercial virtual implant planning software is significantly influenced by the preprocessing of imported data, by the user and by the number of restorations resulting in clinically non-acceptable deviations encoded in drilling guides.

RapidSplint: virtual splint generation for orthognathic surgery - results of a pilot series

Background

Within the domain of craniomaxillofacial surgery, orthognathic surgery is a special field dedicated to the correction of dentofacial anomalies resulting from skeletal malocclusion. Generally, in such cases, an interdisciplinary orthodontic and surgical treatment approach is required. After initial orthodontic alignment of the dental arches, skeletal discrepancies of the jaws can be corrected by distinct surgical strategies and procedures in order to achieve correct occlusal relations, as well as facial balance and harmony within individualized treatment concepts. To transfer the preoperative surgical planning and reposition the mobilized dental arches with optimal occlusal relations, surgical splints are typically used. For this purpose, different strategies have been described which use one or more splints. Traditionally, these splints are manufactured by a dental technician based on patient-specific dental casts; however, computer-assisted technologies have gained increasing importance with respect to preoperative planning and its subsequent surgical transfer.

Methods

: In a pilot study of 10 patients undergoing orthognathic corrections by a one-splint strategy, two final occlusal splints were produced for each patient and compared with respect to their clinical usability. One splint was manufactured in the traditional way by a dental technician according to the preoperative surgical planning. After performing a CBCT scan of the patient’s dental casts, a second splint was designed virtually by an engineer and surgeon working together, according to the desired final occlusion. For this purpose, RapidSplint®, a custom-made software platform, was used. After post-processing and conversion of the datasets into .stl files, the splints were fabricated by the PolyJet procedure using photo polymerization. During surgery, both splints were inserted after mobilization of the dental arches then compared with respect to their clinical usability according to the occlusal fitting.

Results

Using the workflow described above, virtual splints could be designed and manufactured for all patients in this pilot study. Eight of 10 virtual splints could be used clinically to achieve and maintain final occlusion after orthognathic surgery. In two cases virtual splints were not usable due to insufficient occlusal fitting, and even two of the traditional splints were not clinically usable. In five patients where both types of splints were available, their occlusal fitting was assessed as being equivalent, and in one case the virtual splint showed even better occlusal fitting than the traditional splint. In one case where no traditional splint was available, the virtual splint proved to be helpful in achieving the final occlusion.

Conclusions

In this pilot study it was demonstrated that clinically usable splints for orthognathic surgery can be produced by computer-assisted technology. Virtual splint design was realized by RapidSplint®, an in-house software platform which might contribute in future to shorten preoperative workflows for the production of orthognathic surgical splints.

Accuracy and reliability of a novel method for fusion of digital dental casts and Cone Beam Computed Tomography scans

Several methods have been proposed to integrate digital models into Cone Beam Computed Tomography scans. Since all these methods have some drawbacks such as radiation exposure, soft tissue deformation and time-consuming digital handling processes, we propose a new method to integrate digital dental casts into Cone Beam Computed Tomography scans. Plaster casts of 10 patients were randomly selected and 5 titanium markers were glued to the upper and lower plaster cast. The plaster models were scanned, impressions were taken from the plaster models and the impressions were also scanned. Linear measurements were performed on all three models, to assess accuracy and reproducibility. Besides that, matching of the scanned plaster models and scanned impressions was done, to assess the accuracy of the matching procedure. Results show that all measurement errors are smaller than 0.2 mm, and that 81% is smaller than 0.1 mm. Matching of the scanned plaster casts and scanned impressions show a mean error between the two surfaces of the upper arch of 0.14 mm and for the lower arch of 0.18 mm. The time needed for reconstructing the CBCT scans to a digital patient, where the impressions are integrated into the CBCT scan of the patient takes about 15 minutes, with little variance between patients. In conclusion, we can state that this new method is a reliable method to integrate digital dental casts into CBCT scans. As far as radiation exposure, soft tissue deformation and digital handling processes are concerned, it is a significant improvement compared to the previously published methods.