Maal T J J, Plooij J M, Rangel F A, Mollemans W, Schutyser F A C, Bergé S J
3D Facial Imaging Research Group Nijmegen-Bruges, Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
Int J Oral Maxillofac Surg. 2008 Jul;37(7):641-6. doi: 10.1016/j.ijom.2008.04.012. Epub 2008 Jun 9.
The state-of-the-art diagnostic tools in oral and maxillofacial surgery and preoperative orthodontic treatment are mainly two-dimensional, and consequently reveal limitations in describing the three-dimensional (3D) structures of a patient's face. New 3D imaging techniques, such as 3D stereophotogrammetry (3D photograph) and cone-beam computed tomography (CBCT), have been introduced. Image fusion, i.e. registration of a 3D photograph upon a CBCT, results in an accurate and photorealistic digital 3D data set of a patient's face. The purpose of this study was to determine the accuracy of three different matching procedures. For 15 individuals the textured skin surface (3D photograph) and untextured skin surface (CBCT) were matched by two observers using three different methods to determine the accuracy of registration. The registration error was computed as the difference (mm) between all points of both surfaces. The registration errors were relatively large at the lateral neck, mouth and around the eyes. After exclusion of artefact regions from the matching process, 90% of the error was within+/-1.5 mm. The remaining error was probably caused by differences in head positioning, different facial expressions and artefacts during image acquisition. In conclusion, the 3D data set provides an accurate and photorealistic digital 3D representation of a patient's face.
口腔颌面外科和术前正畸治疗中最先进的诊断工具主要是二维的,因此在描述患者面部的三维(3D)结构时存在局限性。新的3D成像技术,如3D立体摄影测量法(3D照片)和锥形束计算机断层扫描(CBCT)已被引入。图像融合,即在CBCT上配准3D照片,可生成患者面部准确且逼真的数字3D数据集。本研究的目的是确定三种不同匹配程序的准确性。对于15名个体,由两名观察者使用三种不同方法对有纹理的皮肤表面(3D照片)和无纹理的皮肤表面(CBCT)进行匹配,以确定配准的准确性。配准误差计算为两个表面所有点之间的差值(mm)。在颈部外侧、口腔和眼睛周围,配准误差相对较大。在从匹配过程中排除伪影区域后,90%的误差在±1.5mm范围内。其余误差可能是由头部位置差异、不同的面部表情以及图像采集过程中的伪影造成的。总之,3D数据集提供了患者面部准确且逼真的数字3D表示。