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Constructing nasal prosthesis morphological data based on a nonrigid registration algorithm
Aonan Wen1, Xiaohui Zhang2, Yong Wang3
1Doctoral student, Center of Digital Dentistry/Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology, Beijing, PR China.
Statement Of Problem:
The provision of a nasal prosthesis is an important method of restoring the morphological integrity of the face in patients with nasal defects. In the digital design and manufacture of nasal prostheses, constructing the nasal prosthesis morphology is a critical procedure that significantly affects the repair outcome; however, studies on constructing nasal prosthesis morphological data are lacking.
Purpose:
The purpose of this study was to assess using the Procrustes Analysis-Nonrigid Iterative Closest Point (PA-NICP) algorithm, which follows the principle of nonrigid registration, to achieve rapid construction of the nasal prosthesis morphological data. The effects of the PA-NICP algorithm and MeshMonk program in constructing nasal prosthesis morphological data were compared.
Material And Methods:
The 3-dimensional (3D) facial data of 30 adult male patients were collected using a 3D facial scanner (FaceSCAN), and 30 total nasal defect 3D facial datasets were constructed using the Geomagic Wrap 2021 software program. The PA-NICP algorithm proposed in this study (experimental group) and the MeshMonk program reported in previous literature (control group), also based on nonrigid registration, were used with the 3D face template developed in previous research to construct nasal prosthesis morphological data for the total nasal defect 3D facial datasets. The 3D morphological deviation between the nasal prosthesis data and the patient's original nasal morphology, the edge tightness between the nasal prosthesis data and the nasal defect data, and the edge surface continuity of the nasal prosthesis data were calculated. The experimental and control groups were compared with paired-sample statistical analysis (α=.05).
Results:
Regarding the 3D morphological deviation, the root mean square (RMS) value of the 3D deviation was 1.51 ±0.45 mm in the experimental group and 1.34 ±0.31 mm in the control group, with no statistically significant difference between them (P=.054). Regarding the edge tightness, the RMS value of the curve deviation was 0.22 ±0.05 mm in the experimental group and 0.38 ±0.09 mm in the control group, with a statistically significant difference between them (P<.001). Regarding the edge surface continuity, the average percentage of tangent continuous surfaces was 95.47% in the experimental group and 92.20% in the control group, with a statistically significant difference between them (P=.001).
Conclusions:
Both the PA-NICP algorithm and MeshMonk program can construct relatively optimal nasal prosthesis data. The nasal prosthesis data constructed using the PA-NICP algorithm exhibited better edge tightness and morphological transition effects.
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