Objectives: Three-dimensional models of mandibular condyles provide a way for condylar remodeling follow-up. The overall aim was to develop and validate a user-friendly workflow for cone beam CT (CBCT)-based semi-automatic condylar registration and segmentation.
Methods: A rigid voxel-based registration (VBR) technique for registration of two post-operative CBCT-scans was tested. Two modified mandibular rami, with or without gonial angle, were investigated as the volume of interest for registration. Inter- and intraoperator reproducibility of this technique was tested on 10 mandibular rami of orthognathic patients by means of intraclass correlation coefficients (ICC's) and descriptive statistics of the transformation values from the VBR. The difference in reproducibility between the two modified rami was evaluated using a paired -test ( < 0.05). For the segmentation, eight fresh frozen cadaver heads were scanned with CBCT and micro-CT. These data were used to test the inter- and intraoperator reproducibility (ICC's) and accuracy (Bland-Altman plot) of a newly designed workflow based on semi-automated contour enhancement.
Results: Excellent ICC's (0.94-0.99) were obtained for the voxel-based registration technique using both modified rami. If the gonial angle was not included in the volume of interest, there was a trend of increased operator error suggested by significant higher interoperator differences in translation values ( = 0,0036). The segmentation workflow proved to be highly reproducible with excellent ICC's (0.99), low absolute mean volume differences between operators (23.19 mm), within operators (28.93 mm) and low surface distances between models of different operators (<0.20 mm). Regarding the accuracy, CBCT-models slightly overestimate the condylar volume compared to micro-CT.
Conclusions: This study provides a validated user-friendly and reproducible method of creating three-dimensional-surface models of mandibular condyles out of longitudinal CBCT-scans.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068073 | PMC |
| http://dx.doi.org/10.1259/dmfr.20190364 | DOI Listing |
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