얼굴 전산화단층촬영 사진을 이용한 안구용적측정치와 안축장과의 상관관계
Semi-automatic Measurement of Ocular Volume from Facial Computed Tomography and Correlation with Axial Length
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목적: 얼굴 전산화단층촬영 영상과 반자동화 프로그램을 이용하여 안구용적을 측정하고 안구용적의 안축장과의 상관관계 및 안구용적을 추정하는 회귀분석을 연구하고자 하였다. 대상과 방법: 얼굴 전산화단층촬영을 시행한 20명, 40안을 대상으로 반자동화 프로그램(MATLAB r2009a, MathWorks, Inc., Natick, MA, USA)을 이용하여 얼굴 전산화단층촬영 사진의 축영상과 관상영상의 안구 단면적을 각각 측정하였고, 측정 단면적을 누적하여 안구용적을 계산하였다. 안축장은 A-scan 초음파를 이용하여 측정되었으며, 안구용적과 안축장 간 회귀분석을 포함한 통계적 분석을 시행하였다. 결과: 안구용적의 측정 결과, 남성은 평균 7.16 ± 1.80 cm3, 여성은 평균 7.24 ± 3.38 cm3였다. 또한 안축장의 측정 결과 남성은 평균 23.47 ± 0.69 mm, 여성은 평균 23.23 ± 1.64 mm로 측정되었으며 안구용적과 안축장은 남녀 간 유의한 차이가 없었다. 안구용적과 안축장은 Pearson 상관계수와 전방깊이를 고려한 편상관계수에서 모두 유의한 양의 상관관계를 보였으며 단순회귀분석을 통해 유의한 통계식이 도출되었다(안구용적[cm3] = 0.0056558 x 안축장3 [mm3] – 0.1798106 x 안축장2 [mm2] + 32.9008570 [p<0.001, R2=0.384]). 결론: 본 연구의 안구용적측정법은 비침습적인 얼굴 전산화단층촬영을 이용한 것으로 특별한 장치 없이 안구용적을 측정하는데 매우 유용한 도구이다. 본 연구의 안축장과의 통계식을 통해 안구용적의 추정 또한 정확하게 할 수 있을 것으로 기대되며 이들을 통해 여러 안질환 및 수술 전후 안구용적에 대한 추가 연구에 도움이 될 수 있을 것으로 생각된다.
Abstract
Purpose: To measure the ocular volume from facial computed tomography (CT) scans using a semi-automatic computer program, and to analyze possible correlations between the axial length and ocular volume using regression analysis. Methods: Forty eyes from 20 facial CT scans were used to measure the ocular volumes. The cross-sectional ocular areas were calculated using a semi-automatic program based on MATLAB r2009a (MathWorks, Inc., Natick, MA, USA), and the ocular volumes were calculated from serial cross-sectional areas. The axial lengths were measured by A-scan ultrasound. Statistical analysis including regression analysis was used to determine possible correlations between the ocular volumes and axial lengths. Results: The mean ocular volumes measured in males and females were 7.16 ± 1.80 cm3 and 7.24 ± 3.38 cm3, respectively. The mean axial lengths measured in males and females were 23.47 ± 0.69 mm and 23.23 ± 1.64 mm, respectively. There were positive correlations using Pearson’s correlation coefficient and the partial correlation coefficient adjusted by axial length. Using regression analysis, the following statistically significant equation was derived: (ocular volume [cm3] = 0.0056558 × axial length3 [mm3] ‐ 0.1798106 × axial length2 [mm2] + 32.9008570 [p < 0.001, R2 = 0.384]). Conclusions: The ocular volume measurement tool in this study was noninvasive and very useful, without special equipment. Accurate estimation of ocular volumes by a statistical equation was feasible, and these findings may be helpful in further study of various ocular diseases and in predicting preoperative and postoperative ocular volumes.
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