Name: digital hybrid model preparation for virtual planning of reconstructive dentoalveolar surgical procedures
Uploaded: 2021-08-05T14:15:00
Description: Watch this Scientific Journal Video about Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures at JoVE.com

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
<ol>
	<li>Load DICOM files into the software
	<ol>
		<li>Download the newest version of the medical imaging software and open it. 
		NOTE: After opening the software, the home screen will appear....

<ol>
	<li>Jacobs, R., Salmon, B., Codari, M., Hassan, B., Bornstein, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cone+beam+computed+tomography+in+implant+dentistry:+recommendations+for+clinical+use.">Cone beam computed tomography in implant dentistry: recommendations for clinical use.</a> BMC Oral Health. 18 (1), 88 (2018).</li><li>Mangano, F., Gandolfi, A., Luongo, G., Logozzo, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intraoral+scanners+in+dentistry:+a+review+of+the+current+literature.">Intraoral scanners in dentistry: a review of the current literature.</a> BMC Oral Health. 17 (1), 149 (2017).</li><li>Pauwels, R., Araki, K., Siewerdsen, J. H., Thongvigitmanee, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Technical+aspects+of+dental+CBCT:+state+of+the+art.">Technical aspects of dental CBCT: state of the art.</a> Dentomaxillofacial Radiology. 44 (1), 20140224 (2015).</li><li>Queiroz, P. M., Santaella, G. M., Groppo, F. C., Freitas, D. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metal+artifact+production+and+reduction+in+CBCT+with+different+numbers+of+basis+images.">Metal artifact production and reduction in CBCT with different numbers of basis images.</a> Imaging Science in Dentistry. 48 (1), 41-44 (2018).</li><li>Scarfe, W. C., Azevedo, B., Pinheiro, L. R., Priaminiarti, M., Sales, M. A. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+emerging+role+of+maxillofacial+radiology+in+the+diagnosis+and+management+of+patients+with+complex+periodontitis.">The emerging role of maxillofacial radiology in the diagnosis and management of patients with complex periodontitis.</a> Periodontology 2000. 74 (1), 116-139 (2017).</li><li>Fedorov, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+Slicer+as+an+image+computing+platform+for+the+Quantitative+Imaging+Network.">3D Slicer as an image computing platform for the Quantitative Imaging Network.</a> Magnetic Resonance Imaging. 30 (9), 1323-1341 (2012).</li><li>Palkovics, D., Mangano, F. 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J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Algorithm+for+planning+a+double-jaw+orthognathic+surgery+using+a+computer-aided+surgical+simulation+(CASS)+protocol.+Part+1:+planning+sequence.">Algorithm for planning a double-jaw orthognathic surgery using a computer-aided surgical simulation (CASS) protocol. Part 1: planning sequence.</a> International Journal of Oral Maxillofacial Surgery. 44 (12), 1431-1440 (2015).</li><li>Xia, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Algorithm+for+planning+a+double-jaw+orthognathic+surgery+using+a+computer-aided+surgical+simulation+(CASS)+protocol.+Part+2:+three-dimensional+cephalometry.">Algorithm for planning a double-jaw orthognathic surgery using a computer-aided surgical simulation (CASS) protocol. Part 2: three-dimensional cephalometry.</a> International Journal of Oral Maxillofacial Surgery. 44 (12), 1441-1450 (2015).</li><li>Lee, C. Y., Ganz, S. D., Wong, N., Suzuki, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+cone+beam+computed+tomography+and+a+laser+intraoral+scanner+in+virtual+dental+implant+surgery:+part+1.">Use of cone beam computed tomography and a laser intraoral scanner in virtual dental implant surgery: part 1.</a> Implant Dentistry. 21 (4), 265-271 (2012).</li><li>Ganz, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+imaging+and+guided+surgery+for+dental+implants.">Three-dimensional imaging and guided surgery for dental implants.</a> Dental Clinics of North America. 59 (2), 265-290 (2015).</li><li>G&#252;th, J. F., Kauling, A. E. C., Schweiger, J., K&#252;hnisch, J., Stimmelmayr, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virtual+simulation+of+periodontal+surgery+including+presurgical+CAD/CAM+fabrication+of+tooth-colored+removable+splints+on+the+basis+of+CBCT+Data:+A+case+report.">Virtual simulation of periodontal surgery including presurgical CAD/CAM fabrication of tooth-colored removable splints on the basis of CBCT Data: A case report.</a> The International Journal of Periodontics &amp; Restorative Dentistry. 37 (6), 310-320 (2017).</li><li>Pauwels, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effective+radiation+dose+and+eye+lens+dose+in+dental+cone+beam+CT:+effect+of+field+of+view+and+angle+of+rotation.">Effective radiation dose and eye lens dose in dental cone beam CT: effect of field of view and angle of rotation.</a> The British Journal of Radiology. 87 (1042), 20130654 (2014).</li><li>Li, Q., Chen, K., Han, L., Zhuang, Y., Li, J., Lin, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+tooth+roots+segmentation+of+cone+beam+computed+tomography+image+sequences+using+U-net+and+RNN.">Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN.</a> Journal of X-ray Science and Technology. 28 (5), 905-922 (2020).</li><li>Lahoud, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+intelligence+for+fast+and+accurate+3D+tooth+segmentation+on+CBCT.">Artificial intelligence for fast and accurate 3D tooth segmentation on CBCT.</a> Journal of Endodontics. 47 (5), 827-835 (2021).</li><li>Blume, O., Donkiewicz, P., Back, M., Born, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bilateral+maxillary+augmentation+using+CAD/CAM+manufactured+allogenic+bone+blocks+for+restoration+of+congenitally+missing+teeth:+A+case+report.">Bilateral maxillary augmentation using CAD/CAM manufactured allogenic bone blocks for restoration of congenitally missing teeth: A case report.</a> Journal of Esthetic and Restorative Dentistry. 31 (3), 171-178 (2019).</li><li>Hartmann, A., Seiler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Minimizing+risk+of+customized+titanium+mesh+exposures+-+a+retrospective+analysis.">Minimizing risk of customized titanium mesh exposures - a retrospective analysis.</a> BMC Oral Health. 20 (1), 36 (2020).</li><li>Varga, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidance+means+accuracy:+A+randomized+clinical+trial+on+freehand+versus+guided+dental+implantation.">Guidance means accuracy: A randomized clinical trial on freehand versus guided dental implantation.</a> Clinical Oral Implants Research. 31 (5), 417-430 (2020).</li></ol>

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...

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3DSlicer</td>
			<td>3DSlicer (The software was first developed at Queen&#8217;s University Canada and since it is open source it is constantly developed by it&#8217;s community)</td>
			<td>4.13.0-2021-03-19</td>
			<td>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&#8217;s use for dental purposes as well</td>
		</tr>
		<tr>
			<td>Meshmixer</td>
			<td>Autodesk Inc.</td>
			<td>3.5</td>
			<td>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.</td>
		</tr>
	</tbody>
</table>

digital hybrid model preparation for 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.

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...

Watch this Scientific Journal Video about Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures at JoVE.com

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

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 ...

Realistic virtual models can show periodontal and alveolar rich defects in three dimensions. Therefore, they can aid the surgical treatment process and provide a deeper understanding on postoperative healing mechanisms. Compared to previous and existing methods, the current approach displays each anatomical structure independently.Therefore, these 3D virtual models represent the real clinical scenario realistically. This method helps to overcome the limitations of traditional diagnostic processes. For example, dental implant placement can be planned on 3D models instead of plenary images of the CBCT scans.The current protocol can be applied in other fields of dentistry, such as endodontic microsurgery, orthotic surgery, and reconstructive surgery following facial tumor resection. Begin with the process segmentation by accessing the segment editor module, select the previously created cropped volume as the master volume of the active segmentation. Use Add to add and Remove to remove segments.Then rename the segments according to the anatomical structure they will represent. Start the segmentation of the alveolar bone by opening the list of effects and selecting Level Tracing, a semi-automatic tool that outlines the region where pixels have the same background value as the selected pixel. Next, drag the mouse to the perimeter of the bone on one of the 2D views, and press the left mouse button to generate the segment on the selected slice of the dataset.Then use the Paint and Erase Hand Tools to modify the segment and correct mistakes. Outline teeth and implants using the Erase tool and delete all highlighted pixels representing them. Repeat the process on every fifth slice of the dataset in the selected orientation.Upon completing the outlining process, compute the missing segments by selecting Fill Between slices from the Effects list and click Initialize to activate contour interpolation. If the results are satisfactory, click Apply. Then scroll through the dataset upon completion to check and correct occasional mistakes.Use the Smoothing effect by selecting Median as the smoothing method. Then set the kernel size to five by five by five pixels by adjusting the millimeter value in the bracket and clicking Apply to make segment boundaries smoother by removing protrusions. Once the segmentation of alveolar bone is completed, repeat the same steps for the segmentation of teeth.Select segmentation from the dropdown bar to add the STL file of the intraoral scan as segmentation. Move the cursor over the module and from the sidebar, select the Fiducial Registration Wizard. From the dropdown menus in both, the From Fiducials and To Fiducial sections, choose Create New MarkupsFiducial.In the From section next to the dropdown bar, use the Place a Markup Point icon to place marker points on well-defined anatomical landmarks on the intraoral optical scanning or IOS. Markup points will be numbered in order of placement. Place markers in the same position to create the To list.And in the same order on the cone-beam computed tomography or CBCT dataset, markup points with the same number must represent the same anatomical landmark. After the To lists are ready, access the dropdown menu in the Registration Result Transform section of the sidebar and select Create New Linear Transform to create a transformation. Access the transforms module and select the previously created transformation as the act of transform.In the Apply Transform section, move the IOS segmentation and the From Markups list from the Transformable box to the Transformed box. This step will help to superpose the IOS over the CBCT dataset. Open the computer-aided design or CAD software, and click Import on the home screen.Then select the STL models previously exported from the DICOM image processing software. Go to Sculpt in the menu bar. And from the brush inventory, select the Adaptive Reduce to refine imported models.In the sidebar, click on the Select tab and choose Brush as the selection tool. Then use Unwrap Brush mode and adjust the size of the brush. Using the brush, select the crown of each tooth until the marginal gingiva is on the IOS.From Modify tab, select Smooth Boundary and click Apply if the results are satisfactory. Go to Select and choose Edit and Separate to create individual object from the selected area. Next, go to Analysis in the menu bar and select Inspect.Select Flat Fill as the whole fill mode, and click Auto Repair All to create closed models from the IOS model and the separated teeth models. In the Sculpt menu, choose Shrink Smooth Brush and smooth out the edges of the filled hole. Use the Shrink Smooth Brush on the segmented tooth model until teeth are completely covered by the tooth crowns separated from the IOS.In the object browser, select both the separated crown and the segmented model of the same tooth. In the popup sidebar, select Boolean Union and click Accept. Use Shrink Smooth to smooth the transition.Select both bone and soft tissue models in the object browser, and then opt for the Boolean difference. Using the same process and smooth transitions as described, subtract teeth from the soft tissue model to represent the clinical situation realistically. Color the surfaces of the models by selecting Sculpt from the sidebar, and then switching the little slider from Volume to Surface.From the brush inventory, go to Paint Vertex and use the color wheel in the Color section to select the desired color. Color the surface of each model. The representative analysis shows the outlining of the pixeled region of interest with the same background value with a yellow line.The semi-automatic segmentation tool of level tracing was used in sagittal orientation, followed by subsequent manual segmentation. The results of the semi-automatic segmentation were refined with the help of manual tools, such as Paint and Erase. Finished segmentation was observed in axial, sagittal, and coronal view, and the 3D model was generated automatically from the segments created previously.Super imposition of IOS and subsequent CAD modeling allowed for viewing the clinical situation in three dimensions. Level tracing is an efficient and smart edge detection tool. However, due to artifacts and scatter, the created segments may still need to be modified manually.This process is relatively new. However, the results so far are very promising. 3D models could have a huge potential in the planning, execution, and evolution of surgical procedures in dental surgery and periodontology.

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