Create virtual dentoskeletal model by superimposing digital dental cast into cone-beam computed tomography scan

Reem Shakir Mahmood; Sadiq Jafer Abbas Hamandi; Akmam Hamdy Al-Mahdi

doi:10.1007/s11548-024-03111-4

Create virtual dentoskeletal model by superimposing digital dental cast into cone-beam computed tomography scan

Int J Comput Assist Radiol Surg. 2024 Apr 10. doi: 10.1007/s11548-024-03111-4. Online ahead of print.

Authors

Reem Shakir Mahmood¹, Sadiq Jafer Abbas Hamandi², Akmam Hamdy Al-Mahdi³

Affiliations

¹ Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq. reem.sh.mahmood@nahrainuniv.edu.iq.
² Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
³ Medical City - Oral and Maxillofacial Surgery Department BDS, FICMS, HiDLM, MFDSRCPS (Glas.), FDCRCS (Eng), Baghdad, Iraq.

PMID: 38600410
DOI: 10.1007/s11548-024-03111-4

Abstract

Purpose: Many artifacts and obstacles associated with cone-beam computed tomography (CBCT) scan can obscure or distort the details of the teeth and occlusal surface, like distorted teeth, streak artifacts, noise, and some malocclusion cases with excessive overlapping between jaws cause decrease the interocclusal space, which can impact diagnosis and treatment planning, and the 3D reconstruction accuracy. Optimizing dental precision by Integrating CBCT scans with other imaging modalities, supply more information to enhance CBCT accuracy, mainly in dental areas with limited clarity.

Methods: Performing the Structure-from-Motion (SfM) photogrammetry method, using phone camera and photograph studio setup using simple hardware, to digitize the dental casts and obtain an accurate digital dental model. Using this digital dental model to enhance dental precision in the CBCT data by performing the superimposition process, using a surface-based registration method and integration process to create a virtual dentoskeletal model. Evaluate the accuracy and quality of the superimposition results using qualitative (visual inspection) and quantitative measures.

Results: The differences between the virtual dentoskeletal model and the reference CBCT model are calculating by the 3D Euclidean distance, the mean ± SD are 0.212 ± 0.169 mm and 0.26 ± 0.149 mm for the maxilla and mandible, respectively. The color-coded map shows that the two surfaces are similar, but the extremist values are concentrated in the dental region due to the presence of the noise in the reference model and the gingiva in the virtual dentoskeletal model.

Conclusions: The resulting virtual dentoskeletal model can be viewed and manipulated on a computer screen, allowing for a detailed analysis of the teeth and supporting structures. The 3D model generated by the SfM photogrammetry technique did well during the superimposition process, representing a reliable method for virtual-based processing such as orthognathic surgery planning and splint design.

Keywords: Accuracy; CBCT scan; Digital dental cast; SfM photogrammetry method; Surface-based registration method; Virtual dentoskeletal model.