Various recently introduced minimally invasive treatment modalities are now widely used for enhancing the aging face. In a special, filler is used to increase the volume of tissue, and so understanding the regional thickness and distribution of the facial superficial fat is essential for optimizing minimally invasive procedures. The aim of this study was to establish the overall facial skin and superficial fat thicknesses using a three-dimensional (3D) scanning system. From 53 adult Korean and Thai embalmed adult cadavers, the undissected and serially-dissected facial specimens were scanned and reconstructed. The facial skin and superficial fat thicknesses on seven facial regions were calculated from the superimposed images. The facial skin tended to become thicker in the order of the radix and dorsum, and the temple, supraorbital, forehead, perioral, cheek, and infraorbital areas. The skin was thinnest at radix and dorsum (1.51 ± 0.55 mm), and thickest in infraorbital region (1.97 ± 0.84 mm). The facial superficial fat thickness tended to increase in the order of the radix and dorsum, supraorbital, forehead, temple, cheek, infraorbital, and perioral regions. The superficial fat was thinnest at the radix and dorsum (1.61 ± 1.07 mm), and thickest in the perioral region (5.14 ± 3.31 mm). The facial superficial fat thickness tended to increase in the order of the radix and dorsum, supraorbital, forehead, temple, cheek, infraorbital, and perioral regions. The present findings indicate that 3D scanning system can yield crucial anatomical information about depths of the facial skin and superficial fat layers for utilization in various clinical procedures. Clin. Anat. 32:1008–1018, 2019.
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The authors are grateful to the cadaver donors and their families who participated in the donation program. We also thank Heungsan Choi (CEO) and Jinsu Kim (CTO) of Morpheus company, for their technical assistance during 3D data acquisition. This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B4003781).
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