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Geomagic wrap

Manufactured by 3D Systems
Sourced in United States

Geomagic Wrap is a software solution for 3D data processing and reverse engineering. It is designed to capture, clean, and prepare 3D scan data for downstream applications. The core function of Geomagic Wrap is to convert point cloud and mesh data into high-quality 3D models.

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11 protocols using geomagic wrap

1

Surface Model Reconstruction from μCT Scans

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The μCT scans were segmented and surface models were extracted using Amira software (Thermo Fisher Scientific, version 6.0.0 [114 ]). The inner structure of the surface models was removed using Meshlab [115 ]. Final tidying and decimation of the surface models to 500,000 triangles were made under Geomagic wrap (3D Systems 2017).
Anatomical landmarks and curves were digitized using Morphodig 1.5.4 [116 ]. For a detailed map and designation of digitized anatomical landmarks and curves, please refer to Additional file 1: Tables S1–2 and Figs. S1–2. All specimens were measured twice to reduce potential digitization errors. One scan for each bone was arbitrarily chosen to serve as a template to allow semi-automated placement of surface sliding landmarks onto all surface models. The specimen Callithrix argentata AMNH 184689 was chosen as a template for the humerus and the specimen Callimico goeldii FMNH 153714 for the femur. The same points and curves, as in all specimens, were placed on template and surface sliding landmarks were digitized using IDAV Landmark [117 ].
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2

High-Resolution Scanning and 3D Modeling of Pierolapithecus Cranium

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The holotype cranium of Pierolapithecus (IPS21350.1) is housed at the Institut Català de Paleontologia Miquel Crusafont (ICP) in Sabadell, Spain. The specimen was transported to Centro Nacional de Investigación sobre la Evolución Humana (CENIEH, Burgos, Spain) for high resolutions scanning. Avizo (v. 7; FEI-Visualization Sciences Group Inc.) was used to visualize the CT images and to define and segment individual bone fragments. Each of the defined segments (Fig. 1 and SI Appendix, Fig. S22) was converted to PLY format and exported from Avizo into Geomagic Wrap (2021; 3D Systems), where the surfaces were smoothed and converted into surface models. The separated bone fragments were repositioned in Geomagic Wrap. See SI Appendix, Extended Methods for details and data sharing plan.
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3

Wear Characterization of Prosthetic Menisci

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The wear characterization of the menisci was performed using the software Geomagic Wrap and Geomagic Control X (3D Systems, Inc., Rock Hill, SC, USA).
The 3D digital models of the worn menisci were compared with their corresponding checks, point by point, and all the deviations were registered. The method used for the wear characterization consisted of various steps. At the beginning, the reference check mesh and the worn 3D digital model were aligned and registered, according to the following pairs: #1 – #A; #2 – #B; #3 – #B. The registration was done using the check model as a reference and superimposing the worn model on top of this. The alignment method was based on the reference unworn surfaces, applying first a feature by feature registration and then a best fit global registration. The output of this procedure is a series of 3D wear maps, showing the distribution of material loss over the prosthesis surface, data about the wear depth, the friction area, volumetric loss and volumetric wear rates.
Figure 3 reports the flowchart of the whole wear characterization procedure, here described, and performed after the completion of the previous steps: identification of inserts and corresponding checks and wear tests at the simulator.
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4

3D Segmentation and Reconstruction of CT Scans

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We segmented the CT scans using Avizo (v. 9.7, FEI Visualisation Sciences Group). For each specimen, we only retained the right side; when the right side was not available, we segmented the left side and mirrored it, using the function “mirror” in the Blender software (v. 3.0, Blender Online Community, 2014). The 3D models were imported in Geomagic Wrap (v. 2013.001, 3D Systems, 2013). We used the function “remesh”, and “clean” to remove and clean artefacts manually, making sure the initial shape of the specimen was preserved.
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5

Midpalatal Suture Analysis from CBCT Scans

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Data preparation comprised several steps, as depicted in Fig. 1. First, the CBCTs were converted into anonymised DICOM files and exported using RadiAnt DICOM Viewer [22 ]. Second, the exported files were reconstructed in Avizo v.9.3 software (FEI, Hillsboro, OR, USA) and Geomagic Wrap (3D Systems) so that only the cranial segments were displayed in the three-dimensional isosurface, based on case-specific thresholds to optimise sutural traceability. Third, the midpalatal suture was photographed using uniform positioning to control the degree of parallax in the R package ‘rgl’ [23 ]. To check for robustness, the data were additionally prepared using the open-source software 3DSlicer [24 (link)] as an alternative to Avizo and Geomagic. These steps produced two-dimensional digital photographs of the midpalatal suture, which were used for the subsequent analysis.

Process of data preparation and analysis. Note: The figure shows, from left to right, CBCTs viewed in RadiAnt DICOM Viewer, segmentation of bony structures in Avizo, processing of three-dimensional surfaces in Geomagic and landmarking of two-dimensional photographs using R packages. Abbreviations: CBCT, cone-beam computed tomography

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6

Evaluating Geometry Reconstruction Reproducibility

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The geometries were compared using the deviation tool in Ansys 2020 R1 Spaceclaim and Geomagic Wrap 2021 (3D Systems, Inc.). To investigate the reproducibility of the geometry reconstruction the subsequent comparisons have been done (A–B; A–C; A–D; B–C; B–D; C–D). The first operator mentioned is the source and the second is the target (Source to Target). Switching the comparison order would only produce a negative difference and so was not investigated. Colour contours are plotted with a tolerance of 0.01 mm represented in green, inside tolerance (IT) represented in blue and outside tolerance (OT) represented in red. Root mean square (RMS) estimates of the deviations in geometry were extracted from Geomagic Wrap [2021 3D Systems, Inc.]. To investigate the shape changes at different timepoints the following comparisons were done for geometries reconstructed by operator D (Timepoint I–II; II–III; III–IV; IV–V; V–VI).
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7

Evaluating Expander Volume Accuracy

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For expander volume measurement, two specifications (50 ml and 100 ml) of kidney-shaped soft tissue expanders (Jiusheng Medical Supply, Yuyao, China), which are most commonly applied in expansion auricular reconstructive surgery, were used in this study (Fig. 7).

The specifications of the two tissue expanders that are most commonly used in auricular reconstruction. Left 100 ml, right 50 ml.

Three 50 ml expanders were injected with 50 ml, 60 ml, and 70 ml of saline. Five 100 ml expanders were injected with 80, 90, 100, 110, and 120 ml of saline. All eight expanders underwent computed tomography (CT) after injection (Brilliance CT 64 slice, Philips Medical Systems, Cleveland, OH; tube voltage, 120 kVp; tube current, 220 mAs; collimation, 0.6 mm; pitch, 0.8; rotation time, 0.75 s; matrix, 512 512; and field of view, 350 mm). DICOM data were then acquired and imported to ProPlan CMF 3.0 (Materialise NV, Leuven, Belgium), where the injection hose and injection pots were removed manually and STL files of expanders were created. All STL files were then imported into Geomagic Wrap 2015 (3D Systems Inc., Rock Hill, USA), and the surface area of the expanders was measured automatically using the software (Fig. 8).

DICOM data of the expanders were processed in ProPlan (above), and the surface area was measured in Geomagic Wrap (below).

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8

Feather Surface Reconstruction for Optical Simulation

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The external surface of each feather was segmented in VGStudioMAX 2.0 (Volume Graphics) and a 3D polygonal mesh comprising a geometric model of the external surface was extracted using the QuickMesh setting and exported as an OBJ file. To optimize the ray-tracing simulations, each polygonal model was cropped to a 500-µm by 500-µm swatch of the feather vane and then the triangle count was further reduced using the decimate feature (tolerance set to 325 nm) in Geomagic Wrap (3D Systems). Finally, we used the Mesh Doctor feature in Geomagic Wrap to make the surface model manifold, i.e., “water tight.” This last step was necessary to repair any defects in the polygonal mesh through which simulated rays could artifactually enter and become trapped inside the feather during ray-tracing simulations.
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9

Comparative Limb Morphology for Leaping Ability

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The segmented CT scans were exported as a surface mesh from Amira. Measurements of the outer morphology of the humerus, ulna, radius, femur, and tibia were obtained on these meshes in Geomagic Wrap (3D Systems 2017). We measured the effective length of the humerus, radius, femur, and tibia to calculate the intermembral index (IMI: (humeral length + radial length) / (femoral length + tibial length)). A low IMI indicates relatively longer hindlimbs and hence, a specialization for vertical leaping [78 ]. We further measured robustness variables that inform on the potential to resist stresses as well as the length of muscle in-levers that inform on the potential to generate and absorb joint torques. A detailed description and depiction of the measurements are provided in the supporting information (Figs. S2, S3, S4, S5, S6) and a brief overview of all measurements is displayed in Fig. 1. All internal and external morphological variables were continuous and we size-corrected them prior to further analysis using the centroid sizes of the humerus and femur (see Supporting information note 2 for details).
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10

Upper Extremity Anatomy Modeling

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Nine fresh-frozen, male, right upper-extremities (average: 58 yrs., range: 44-67 yrs.) were confirmed to be free of traumatic defects or inflammatory bone changes using fluoroscopy. The specimens were thawed to room temperature prior to use. The specimen preparation, computed tomography (CT) imaging and generation of three dimensional bone models were implemented as described previously (Badida et al., 2020 (link)). Point coordinates of the anatomical landmarks on the radius and metacarpals and their respective fiducial markers were digitized on the bone models using Geomagic Wrap (3D Systems®, NC, US). The anatomical and fiducial landmarks utilized for registration and transformation of anatomical coordinate systems from CT images (CTGCS) to the robot’s global coordinate system (ROBGCS) were identified as described previously (Badida et al., 2020 (link)).
The surface models of the third metacarpal and capitate were used to calculate bone lengths and revised carpal height ratios (Nattrass et al., 1994 (link)). Revised carpal height ratio is defined as the carpal height divided by the capitate length. Carpal height was defined as the distance between the articular surface of the radius and the most distal point on the surface of the capitate (i.e., the proximal-distal distance between their coordinate systems).
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