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A workflow for creating three-dimensional (3D) virtual hybrid models has been designed based on cone-beam computed tomography dataset and intraoral optical scans utilizing radiographic image segmentation methods and free-form surface modeling. Digital models are used for the virtual planning of reconstructive dentoalveolar surgical procedures.
Virtual, hybrid three-dimensional (3D) model acquisition is presented in this article, utilizing the sequence of radiographic image segmentation, spatial registration, and free-form surface modeling. Firstly cone-beam computed tomography datasets were reconstructed with a semi-automatic segmentation method. Alveolar bone and teeth are separated into different segments, allowing 3D morphology, and localization of periodontal intrabony defects to be assessed. The severity, extent, and morphology of acute and chronic alveolar ridge defects are validated concerning adjacent teeth. On virtual complex tissue models, positions of dental implants can be planned in 3D. Utilizing spatial registration of IOS and CBCT data and subsequent free-form surface modeling, realistic 3D hybrid models can be acquired, visualizing alveolar bone, teeth, and soft tissues. With the superimposition of IOS and CBCT soft tissue, thickness above the edentulous ridge can be assessed about the underlying bone dimensions; therefore, flap design and surgical flap management can be determined, and occasional complications may be avoided.
Technological advancements in dentistry have enabled computer-aided treatment planning and simulation of surgical procedures and prosthetic rehabilitation. Two essential methods for 3D data acquisition in digital dentistry are: (1) cone-beam computed tomography (CBCT)1 and (2) intraoral optical scanning (IOS)2. Digital information of all relevant anatomical structures (alveolar bone, teeth, soft tissues) can be acquired using these tools to plan reconstructive dentoalveolar surgical procedures.
Cone-beam technology was first introduced in 1996 by an Italian research group. Delivering significantly lower radiation dose and higher resolution (compared to conventional computed tomography), CBCT has quickly become the most frequently used 3D imaging modality in dentistry and oral surgery3. CBCT is often used to plan different surgical procedures (e.g., periodontal regenerative surgery, alveolar ridge augmentation, dental implant placement, orthognathic surgery)1. CBCT datasets are viewed and can be processed in radiographic imaging software that provides 2D images, and 3D renders-however, most imaging software use threshold-based algorithms for 3D image reconstruction. Thresholding methods set the upper and lower bounds of a voxel grey value interval. Voxels that fall in between these bounds will be rendered in 3D. This method allows speedy model acquisition; however, since the algorithm cannot differentiate anatomical structures from metal artifacts and scattering, the 3D renders are highly inaccurate and have very little diagnostic value4,5. For the reasons mentioned above, many fields within dentistry still rely on conventional 2D radiographs (intraoral radiographs, panoramic X-ray) or the 2D images of CBCT datasets5. Our research group presented a semi-automatic image segmentation method in a recently published article, using open-source radiographic image processing software6 wherein anatomically based 3D reconstruction of CBCT datasets is performed7. With the help of this method, anatomical structures were differentiated from metal artifacts, and, more importantly, alveolar bone and teeth could be separated. Therefore, a realistic virtual model of hard tissues could be acquired. 3D models were used to evaluate intrabony periodontal defects and for treatment planning before regenerative periodontal surgeries.
Intraoral optical surface scanners provide digital information on clinical conditions (clinical crown of the teeth and soft tissues). The original intended purpose of these devices was to directly acquire digital models of patients for the planning and fabrication of dental prostheses with computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies8. However, due to the wide range of applications, their use was quickly implemented in other fields of dentistry. Maxillo-facial surgeons combine IOS and CBCT into a hybrid setup that can be utilized for virtual osteotomy and digital planning of orthognathic surgeries9,10. Dental implantology is probably the field that uses digital planning and guided execution most commonly. Navigated surgery eliminates most complications related to implant mispositioning. The combination of CBCT datasets and stereolithography (.stl) files of IOS is routinely used to plan the guided implant placement and the fabrication of static implant drilling guides11,12. Intraoral scans superimposed over CBCT datasets have also been used to prepare esthetic crown lengthening13; however, soft tissues were superimposed only over CBCT datasets reconstructed with thresholding algorithms. Yet, to perform accurate 3D virtual planning of regenerative-reconstructive surgical interventions and dental implant placement, realistic 3D hybrid models of patients must be composed of CBCT and IOS data.
Hence, this article aims to present a step-by-step method to acquire realistic hybrid digital models for virtual surgical planning before reconstructive dentoalveolar surgical interventions.
This study was conducted in complete accordance with the Declaration of Helsinki. Before manuscript preparation, written informed consent was provided and signed by the patient. The patient granted permission for data usage for the demonstration of the protocol.
1. Radiographic image processing
Figure 1: Application of "Level Tracing" semi-automatic segmentation tool in sagittal orientation. (A) Outlining the region of pixels with the same background value with a yellow line. (B) Results of "Level Tracing" and subsequent manual segmentation. (C) Refinement of semi-automatic segmentation with the help of manual tools (paint, erase). Please click here to view a larger version of this figure.
NOTE: Using number keys to allow fast switching between tools.
Figure 2: Morphological contour interpolation with "Fill Between Slices," light green areas indicating the automatically reconstructed part of the segment. (A) Axial view. (B) Sagittal view. (C) Coronal view. Please click here to view a larger version of this figure.
NOTE: Make sure that only the segment is visible on which the interpolation is applied. The visibility of the segments can be toggled in the segments list.
Figure 3: Finished segmentation, the brown segment representing bone and the blue segment representing teeth. (A) Axial view. (B) Sagittal view. (C) Coronal view. (D) The 3D model is generated automatically from the segments created previously. Please click here to view a larger version of this figure.
Figure 4: Spatial registration of IOS by placing fiducial markers on well-defined anatomical landmarks. Please click here to view a larger version of this figure.
NOTE: If necessary, the accuracy of the transformation can be improved by moving the markup points or by adding additional points.
2. Export models as .stl files for free-form surface modeling
3. Free-form surface modeling
Animated Figure 1: Animation of the final, colored model, ready for virtual surgical planning. Please click here to download this Figure.
Virtual allowing three-dimensional (3D) models can be generated using radiographic image segmentation, spatial registration, and free-form modeling. The models digitally depict the clinical situation, making three-dimensional planning of various surgical interventions possible. With separate segmentation of bone and teeth, the boundary between the two anatomical structures is visible, 3D morphology and localization of periodontal intrabony defects are to be assessed. The severity, extent, and morphology of acute and chro...
With the presented protocol, periodontal and alveolar defect morphologies can be visualized in three dimensions (3D), providing a more accurate depiction of the clinical situation than can be achieved by 2D diagnostic methods and 3D models generated with thresholding algorithms. The protocol can be divided into three major phases: (1) semi-automatic segmentation of CBCT datasets, (2) spatial registration of CBCT and IOS, and (3) free-form surface modeling. Technically, segmentation can be performed on any three-dimension...
The authors declare no conflict of interest.
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Name | Company | Catalog Number | Comments |
3DSlicer | 3DSlicer (The software was first developed at Queen’s University Canada and since it is open source it is constantly developed by it’s community) | 4.13.0-2021-03-19 | Open source radiographic image processing software platform. Software is primarily intended for general medicine, however the wide range of segmentation an modelling tools allow it’s use for dental purposes as well |
Meshmixer | Autodesk Inc. | 3.5 | Open source free form surface modelling software developed for prototype development and basic 3D sculpting. However, due to the usefulness of tools for dental purpose, not just 3D models, but even static guides for navigated surgery can be designed. |
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