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A Comparison for Cervical Neural Foraminal Area by 3-dimensional CT in Normal Adults

3차원 컴퓨터단층촬영상을 이용한 정상 성인의 경추 신경공 면적 비교

  • Kim, Yon-Min (Department of Radiotechnology, Wonkwang Health Science University)
  • 김연민 (원광보건대학교 방사선과)
  • Received : 2021.12.08
  • Accepted : 2021.12.20
  • Published : 2021.12.31

Abstract

Cervical foraminal stenosis is a disease in which the nerves that pass from the spinal canal to the limbs are narrowed and the nerves are compressed or damaged. Due to the lack of an imaging method that provides quantitatively stenosis, this study attempted to evaluate the area of the cervical vertebrae by reconstructing a three-dimensional computed tomography image, and to determine the area of the neural foramen in normal adults to calculate the stenosis rate. Using a three-dimensional image processing program, the surrounding bones including the posterior spinous process, lateral process, and lamellar bones of the cervical vertebra were removed so that the neural foramen could be observed well. A region of interest including the neural foraminal area of the three-dimensional image was set using ImageJ, and the number of pixels in the neural foraminal area was measured. The neural foraminal area was calculated by multiplying the number of measured pixels by the pixel size. To measure the largest neural foraminal area, it was measured between 40~50 degrees in the opposite direction and 15~20 degrees toward the head. The average area of the right C2-3 foramen was 44.32 mm2, C3-4 area was 34.69 mm2, C4-5 area was 36.41 mm2, C5-6 area was 35.22 mm2, C6-7 area was 36.03 mm2. The average area of the left C2-3 foramen was 42.71 mm2, C3-4 area was 32.23 mm2, C5-6 area was 34.56 mm2, and C6-7 area was 31.89 mm2. By creating a reference table based on the neural foramen area of normal adults, the stenosis rate of patients with neural foraminal stenosis could be quantitatively calculated. It is expected that this method can be used as basic data for the diagnosis of cervical vertebral foraminal stenosis.

Keywords

Acknowledgement

This paper was supported by Wonkwang Health Science University in 2021

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