SVD와 Bayesian 알고리즘을 이용한 뇌경색 부피 측정에 관한 연구

Study on Volume Measurement of Cerebral Infarct using SVD and the Bayesian Algorithm

급성 허혈성 뇌졸중(Acute ischemic stroke; AIS) 환자는 증상발현 수 시간 이내 영상의학 검사를 통해 뇌경색(Infarction)을 조기 진단하여야 한다. 본 연구에서 SVD와 Bayesian 알고리즘을 이용한 뇌경색의 부피 측정을 관류 전산화단층촬영(Computed tomography perfusion; CTP)과 확산 강조 자기공명영상(Magnetic resonance diffusion weighted image; MR DWI)을 비교하여 임상적 유용성을 알고자 하였다. 2017년 9월부터 2020년 9월까지 급성 허혈성 뇌졸중 증상으로 응급실을 내원한 환자 중 50명(남 : 여 = 33 : 17)의 영상의학 검사 정보를 후향적으로 이용하였다. SVD와 Bayesian 알고리즘으로 측정된 뇌경색 부피는 윌콕슨 부호순위 검정(Wilcoxon signed rank test) 통계분석을 하여 중앙값(Median)과 사분위수(Iter quartile range; IQR) 25 - 75 % 범위로 나타내었다. CTP 검사로 측정한 core volume(단위 : cc)은 SVD가 18.07 (7.76 - 33.98), Bayesian은 47.3 (23.76 - 79.11)으로 측정되었고 penumbra volume은 SVD가 140.24 (117.8 - 176.89), Bayesian은 105.05 (72.52 - 141.98)로 측정되었다. Mismatch ratio (%)는 SVD가 7.56 (4.36 - 15.26), Bayesian은 2.08 (1.68 - 2.77)로 측정되었으며 모든 측정값은 통계적으로 유의미한 차이가 있었다(p < 0.05). 스피어만 상관 분석(Spearman’s correlation analysis) 결과는 CT Bayesian과 MR로 측정한 뇌경색 부피의 상관계수(r = 0.915)가 CT SVD와 MR의 상관계수(r = 0.763)보다 더욱 높은 양의 상관관계를 보였다(p < 0.01). 블랜드 알트만 산점도(Bland altman plot) 분석 결과는 CT Bayesian과 MR로 측정한 뇌경색 부피의 산점도 기울기(y = &#8211; 0.065)가 CT SVD와 MR의 산점도 기울기(y = &#8211; 0.749)보다 완만하게 측정되어 Bayesian이 더 높은 신뢰성을 나타내었다. 따라서 뇌경색 부피의 측정에서 Bayesian 알고리즘이 SVD보다 높은 정확도를 보였으므로 임상에서 유용하게 사용될 것으로 사료된다.

Acute ischemic stroke(AIS) should be diagnosed within a few hours of onset of cerebral infarction symptoms using diagnostic radiology. In this study, we evaluated the clinical usefulness of SVD and the Bayesian algorithm to measure the volume of cerebral infarction using computed tomography perfusion(CTP) imaging and magnetic resonance diffusion-weighted imaging(MR DWI). We retrospectively included 50 patients (male : female = 33 : 17) who visited the emergency department with symptoms of AIS from September 2017 to September 2020. The cerebral infarct volume measured by SVD and the Bayesian algorithm was analyzed using the Wilcoxon signed rank test and expressed as a median value and an interquartile range of 25 - 75 %. The core volume measured by SVD and the Bayesian algorithm using was CTP imaging was 18.07 (7.76 - 33.98) cc and 47.3 (23.76 - 79.11) cc, respectively, while the penumbra volume was 140.24 (117.8 - 176.89) cc and 105.05 (72.52 - 141.98) cc, respectively. The mismatch ratio was 7.56 % (4.36 - 15.26 %) and 2.08 % (1.68 - 2.77 %) for SVD and the Bayesian algorithm, respectively, and all the measured values &#8203;&#8203;had statistically significant differences (p < 0.05). Spearman’s correlation analysis showed that the correlation coefficient of the cerebral infarct volume measured by the Bayesian algorithm using CTP imaging and MR DWI was higher than that of the cerebral infarct volume measured by SVD using CTP imaging and MR DWI (r = 0.915 vs. r = 0.763 ; p < 0.01). Furthermore, the results of the Bland Altman plot analysis demonstrated that the slope of the scatter plot of the cerebral infarct volume measured by the Bayesian algorithm using CTP imaging and MR DWI was more steady than that of the cerebral infarct volume measured by SVD using CTP imaging and MR DWI (y = -0.065 vs. y = -0.749), indicating that the Bayesian algorithm was more reliable than SVD. In conclusion, the Bayesian algorithm is more accurate than SVD in measuring cerebral infarct volume. Therefore, it can be useful in clinical utility.