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Identification of cranial nerve ganglia using sectioned images and three-dimensional models of a cadaver

  • Kim, Chung Yoh (Department of Anatomy, Dongguk University School of Medicine) ;
  • Park, Jin Seo (Department of Anatomy, Dongguk University School of Medicine) ;
  • Chung, Beom Sun (Department of Anatomy, Yonsei University Wonju College of Medicine)
  • Received : 2022.01.12
  • Accepted : 2022.04.06
  • Published : 2022.07.01

Abstract

Background: Cranial nerve ganglia, which are prone to viral infections and tumors, are located deep in the head, so their detailed anatomy is difficult to understand using conventional cadaver dissection. For locating the small ganglia in medical images, their sectional anatomy should be learned by medical students and doctors. The purpose of this study is to elucidate cranial ganglia anatomy using sectioned images and three-dimensional (3D) models of a cadaver. Methods: One thousand two hundred and forty-six sectioned images of a male cadaver were examined to identify the cranial nerve ganglia. Using the real color sectioned images, real color volume model having a voxel size of 0.4 × 0.4 × 0.4 mm was produced. Results: The sectioned images and 3D models can be downloaded for free from a webpage, anatomy.dongguk.ac.kr/ganglia. On the images and model, all the cranial nerve ganglia and their whole course were identified. In case of the facial nerve, the geniculate, pterygopalatine, and submandibular ganglia were clearly identified. In case of the glossopharyngeal nerve, the superior, inferior, and otic ganglia were found. Thanks to the high resolution and real color of the sectioned images and volume models, detailed observation of the ganglia was possible. Since the volume models can be cut both in orthogonal planes and oblique planes, advanced sectional anatomy of the ganglia can be explained concretely. Conclusions: The sectioned images and 3D models will be helpful resources for understanding cranial nerve ganglia anatomy, for performing related surgical procedures.

Keywords

Acknowledgement

This work was supported (in part) by the Yonsei University Wonju Campus Future-Leading Research Initiative of 2021 (2021-52-0057) and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1G1A1092673 and 2021R1F1A1063044).

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