Purpose: The purpose of this study was to generate three-dimensional models based on digital volumetric data that can be used in basic and advanced education. Methods: Four sets of digital volumetric data were established by cone beam computed tomography (CBCT) (Accuitomo, J. Morita, Kyoto, Japan). Datasets were exported as Dicom formats and imported into Mimics and Magic software programs to separate the different tissues such as nerve, tooth and bone. These data were transferred to a Polyjet 3D Printing machine (Eden 330, Object, Israel) to generate the models. Results: Three-dimensional prototype models of certain limited anatomical structures as acquired volumetrically were fabricated. Conclusions: Generating three-dimensional models based on CBCT datasets is possible. Automated routine fabrication of these models, with the given infrastructure, is too time-consuming and therefore too expensiv

Berndt, D.

Lambrecht, J.

Schumacher, R.

Zehnder, M.

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Int J CARS (2009) 4:175–180DOI 10.1007/s11548-008-0275-9ORIGINAL ARTICLEGeneration of three-dimensional prototype models basedon cone beam computed tomographyJ. Th. Lambrecht · D. C. Berndt · R. Schumacher ·M. ZehnderReceived: 28 May 2008 / Accepted: 23 September 2008 / Published online: 15 November 2008© CARS 2008AbstractPurpose The purpose of this study was to generate three-dimensional models based on digital volumetric data thatcan be used in basic and advanced education.Methods Four sets of digital volumetric data were establi-shed by cone beam computed tomography (CBCT) (Accui-tomo, J. Morita, Kyoto, Japan). Datasets were exported asDicom formats and imported into Mimics and Magic soft-ware programs to separate the different tissues such as nerve,tooth and bone. These data were transferred to a Polyjet 3DPrinting machine (Eden 330, Object, Israel) to generate themodels.Results Three-dimensional prototype models of certainlimited anatomical structures as acquired volumetrically werefabricated.Conclusions Generating three-dimensional models based onCBCT datasets is possible. Automated routine fabricationof these models, with the given infrastructure, is too time-consuming and therefore too expensive.Keywords Cone beam computed tomography · Digitalvolume tomography · 3D models · Rapid prototypingIntroductionThe technology of three-dimensional prototype modelingbased on CT or MRI data was introduced into oral andJ. Th. Lambrecht (B) · D. C. Berndt · M. ZehnderDepartment of Oral Surgery, University Hospital for Oral Surgery,Oral Radiology and Oral Medicine, University of Basel,Hebelstraße 3, 4056 Basel, Switzerlande-mail: j-thomas.lambrecht@unibas.chR. SchumacherSchool of Life Sciences, Institute for Medical and AnalyticalTechnologies, University of Applied Sciences NorthwesternSwitzerland, Muttenz, Switzerlandmaxillofacial radiology and surgery over two decades agoBrix and Lambrecht [1] after Marsh and Vannier [2] hadreconstructed the “third dimension” of sequences of high-resolution axial CT scans with algorithms. The models wereused to plan and simulate maxillo-craniofacial surgery inthree dimensions [3] and could be milled from a variety ofmaterials. Laser-hardened acrylic resin had been shown to bea useful alternative [4,5].Cone beam computed tomography (CBCT) was introdu-ced as a high-resolution imaging procedure in oral and maxil-lofacial radiology. Cone beam CT technology has a two-dimensional sensor and uses a cone-shaped X-ray beam inplace of a fan-shaped beam (CT). Volume data can be acqui-red in a single 360◦ rotation of beam and sensor around theskull [6,7]. The characteristics of CBCT are lower entrancedoses and higher resolution in the axial direction comparedwith conventional CT [8–10].Nowadays individual medical models are routinely gene-rated by rapid prototyping. Physical models are produced byselectively solidifying UV-sensitive liquid resin using a laserbeam. The technology was first introduced in mechanicalengineering and has also attracted a great deal of attention inclinical dentistry [11,12]. The aim of this study was to pro-duce three-dimensional individual patient prototype modelsout of CBCT datasets that could eventually be used in basicand advanced education.Materials and methodsGenerating dataDifferent datasets of four patients were established by 3DCBCT (Accuitomo, J. Morita, Kyoto, Japan). One dataset isused as an example in this study. The imaging volume was123176 Int J CARS (2009) 4:175–180a cylinder measuring 40 mm (diameter) × 30 mm (height)at the X-ray rotational center. Images were taken under theexposure conditions of 75–80 kV and 4–6 mA, which are thestandard parameters and were changed for different subjects.The cone beam technique involves a single 360◦ scan inwhich the X-ray source and a reciprocating area detectorsynchronously move around the patient’s head. The top ofthe cone is the source of radiation and the bottom of the coneis the sensor, an image intensifier with a CCD camera. Within17 s the region of interest is scanned in a total of 512 framesof two-dimensional images; these were recorded as an “.avi”format file.i-DixelThe Accuitomo software (i-Dixel images) calculates a volu-metric dataset from 512 single digital images. Out of thisdataset the single slices are calculated from within a volumeof 3 × 4 cm by selecting them in Dicom with a voxel volumeof 0.125 mm × 0.125 mm × 0.125 mm.MimicsThe dataset is imported with a slice thickness of 0.5 mminto the planning software MIMICS (Materialise’s Interac-tive Medical Image Control System, Materialise, Belgium).MIMICS is a link between the scanner data (CT, MRT,CBCT) and a simple virtual 3D representation.To work with the data, a so-called “mask” is generated.The threshold is necessary to create a first separation of thesingle anatomical structures (tooth, bone, nerve). A mask isgenerated for each structure and makes it possible to proceedwithin the work flow. The masks can be edited slice by sliceby simply adding or removing voxels manually. Bone, teethand soft tissue were separated using the above-mentionedfunctions. The masks can be exported as an STL data repre-sentation, which is a simple triangulated 3D surface dataset(Table 1).MagicsThe acquired 3D data can be imported as an STL data-set. Since the major part in the previous process was editedmanually, the resulting surface of the anatomical structureswas relatively rough. For this reason, a remesh was perfor-med. The triangulated surface was smoothened and the edgesof the triangles were assimilated. Using booling operations,figures without overlaps can be generated. These smootheneddata were reimported into MIMICS as an STL dataset.Table 1 Chart showing the workflow1. CBCT Generation of 3-d X-ray data2. i-Dixel Visualisation and selection of Datasets3. MIMICS Addition of 2-d layers to 3-d Datasets4. Magics Surfacesmoothing, Booling ops, Storing5. Polyjet 3D-PrinterDicomPolyjet 3D printingThree-dimensional models are produced by the Polyjetmethod (Objet EDEN 250). Prototypes with high resolutionand excellent surface quality can be produced layer by layer.Figure 1 shows the Polyjet rapid prototype fabrication sys-tem. The high resolution and good surfaces (without “staresteps”) are acquired because of the layer thickness of0.016 mm.UV-sensitive resins are added using printheads in extre-mely thin layers. Adding UV hardens the photopolymer. Inthe same process, supporting material is added that has a gelcharacter and can be removed by water jet at the end of theconstruction process.Fig. 1 The polyjet rapid prototype fabrication system showing thethree axis movement of the components (OBJET, Israel)123Int J CARS (2009) 4:175–180 177Fig. 2 The MIMICS software platform showing the 2D axial (upperleft) coronal (upper right) and sagittal (lower left) dimensions with amask for the gray values of the of the nervus alveolaris inferior (lightblue). The 3D visualization of the three rendered masks shows theimpacted wisdom tooth in close anatomical relation to the alveolar nerve(lower right)ResultsThe MIMICS software uses segmentation algorithms toobtain a 3D visualization at the end. The data which wereacquired by i-Dixel can be read into MIMICS and are readyto be viewed in axial, sagittal and coronal dimensions.The “reslice project” function makes it possible for theuser to change the direction of the slice axes. A correct direc-tion of axis simplifies later use of the data. The “threshold”function enables the user to tell the computer the gray valuewithin the grayscale from which the program should detectand colour the voxels (Fig. 2).The Magics software enables the user to smoothen themanually worked out MIMICS data (Figs. 3, 4).Each generated structure of tooth, nerve and bone wasseparately produced as a model by the Polyjet method(Figs. 5, 6).Thus fabrication of three-dimensional models based onCBCT data is possible. Featuring separate representation ofhard and soft tissue enables the user to realize topographicrelations of anatomic structures.DiscussionConventional CT has been the appropriate technology ofchoice for acquiring datasets in order to fabricate three-dimensional models [13]. The disadvantages are space requi-rements, relatively high doses and high costs [14].By comparison, CBCT, which is nowadays available fordento- maxillo-facial radiological units, requires less space,lower radiation doses, lower costs and offers a higher reso-lution than CT images [15–18]. The latter is due to the voxelsize of the system used. Of course, voxel size is also crucialfor accuracy. Studies on accuracy were done with variousCBCT machines for implants [19] and human skulls andcadavers for the purpose of cephalometric measurements123178 Int J CARS (2009) 4:175–180Fig. 3 Magics representation of a rough surface wisdom tooth[20,21]. With regard to CBCT accuracy with Accuitomo,there is one report by Loubele et al. [22] yielding submillime-ter accuracy for linear measurements on an ex vivo specimen.These results provide the basis for creating haptic modelswith adequate accuracy.Artefact reduction is relevant to creating acceptablemodels [23]. Contrary to CT, CBCT does not provide read-outs in Hounsfield Units (HU). The HU is the standard scalefor measuring conventional CT values and was the basic plat-form for acquiring 3D models where defined tissues could beextracted because of their different HU. Mah and McDavic[24] suggest exporting CBCT data as a raw DICOM data-set and transferring the dataset to UTHSCSA Image Toolfor analysis. Another method described showed that the atte-nuation coefficient of the test material in the phantom wasdetermined with less than one per cent error. This soundspromising for the future and should be the subject of furtherresearch.A grayscale is so far available in our set-up. With theMIMICS software program the aim was to extract certaingray values which were representative of a specific tissue, forexample to discriminate soft tissue (nerve) from hard tissue(bone). To get the real 2D profile of the different tissues it wasnecessary to check every slice manually. Thus the separationof tissues layer by layer became very time-consuming. In thefinal stage individual patient datasets could be visualized.The computer-generated results provided a sufficient basisfor rapid prototyping [25].The use of three-dimensional reconstructions to evaluatethe anatomic relationship of the mandibular canal and impac-ted mandibular third molars has recently been demonstratedby Friedland et al. [26].The technique used in the present study goes one stepfurther and seems to be highly suitable for creating hapticmodels to demonstrate complex anatomic situations, ins-tructing students and planning operations. This technique123Int J CARS (2009) 4:175–180 179Fig. 4 Magics representation after smoothing of the data set shown in Fig. 3Fig. 5 3D models of a segmental piece of the mandible and the wisdomtooth according to the CBCT datasetFig. 6 3D models of the wisdom tooth in close anatomical relationshipto the nervus alveolaris inferior according to the CBCT dataset shownin a mechanical composition for demonstration123180 Int J CARS (2009) 4:175–180could also be used in performing certain dento-alveolar ope-rations. The disadvantage for the time being is a lack ofcost-effectiveness because of the time-consuming procedure.These problems have to be solved before series for basic andadvanced education can be produced.ConclusionTo the best of the authors’ knowledge, this is the first report ongenerating three-dimensional models based on CBCT data-sets. It may become a useful technique for demonstration,planning and teaching, but other options for processing thedatasets and producing models will need to be studied. Theultimate aim will be to use these models as a matter of rou-tine in basic and advanced education, being comparable toconventional CT/MRI-based models.References1. Brix F, Lambrecht JTh (1987) Individuelle Schädelmodell-herstellung auf der Grundlage computertomographischerInformationen. In: Schwenzer N, Pfeifer G (eds) BildgebendesUntersuchungsverfahren in der Mund-, Kiefer- und Gesichts-Chirurgie, Band XXXII. Thieme Verlag Stuttgart, New York,pp 74–772. Marsh JL, Vannier MW (1993) The “third” dimension in craniofa-cial surgery. Plast Reconstr Surg 71:759–7673. Lambrecht JT, Brix F (1990) Individual skull model fabrication forcraniofacial surgery. Cleft Palate J 27:382–3854. Lambrecht JTh, Hammer B, Jacob AL, Schiel H, Hunziker M,Kreusch T, Kliegis U (1995) Individual model fabrication in maxil-lofacial radiology. 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Generation of three-dimensional prototype models based on cone beam computed tomography

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Generation of three-dimensional prototype models based on cone beam computed tomography

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