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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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A new method for the integration of digital dental models and cone-beam computed tomography images

Hsiu-Hsia Lin, Wen-Chung Chiang, Lun-Jou Lo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed

    Abstract:

    This study introduces a regional-surface-based registration without markers for integration of laser-scanned dental images into maxillofacial cone-beam computed tomography (CBCT) images. The method just needs to manually select three similar areas without artifact on the digital dental image and CBCT image, and then the process is automatically complete the fusion (superimposition) of maxillofacial model and the digital dental model. Then the differences such as mean error and root-mean-square (RMS) error are automatically computed between the 2 images according to the selected surfaces and expressed in a color scale. Experimental results show that the mean errors between the 2 models at the integrated model range from 0.15 mm to 0.45 mm and the RMS errors range 0.18 mm to 0.49 mm. The numbers are similar to the results of previous methods and reach a desirable error. Moreover, it is robust feasibility for especially serious artifacts CBT images. It is worth mentioning that all measurements of intra-operator reproducibility and inter-operator reliability are excellent.

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