Bayesian Analysis of Perfusion-weighted Imaging to Predict Infarct Volume: Comparison with Singular Value Decomposition
Bayesian Analysis of Perfusion-weighted Imaging to Predict Infarct Volume: Comparison with Singular Value Decomposition
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DOI：
10.2463/mrms.2013-0085
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发表时间：
2014-01-01
影响因子：
3
通讯作者：
Saski, Makoto
中科院分区：
文献类型：
--
作者：
Kudo, Kohsuke;Boutelier, Timothe;Saski, Makoto
目的：我们利用先前从10只大脑中动脉（MCA）单侧永久性闭塞的食蟹猴获得的数据，比较了贝叶斯估计方法和振荡指数奇异值分解（oSVD）去卷积在预测最终梗死方面的性能。
方法：我们在MCA闭塞3小时后进行基线灌注加权成像，并使用贝叶斯方法和oSVD方法生成达峰时间、通过时间一阶矩、脑血流量、脑血容量和平均通过时间图。最终梗死体积通过MCA闭塞47小时后进行的随访弥散加权成像（DWI）以及组织学标本确定。我们使用具有不同阈值的区域生长技术来确定灌注异常体积。当平均灌注体积与平均最终梗死体积匹配时定义最佳阈值，并在该阈值下计算灌注异常与最终梗死体积之间的皮尔逊相关系数（r）和组内相关系数（ICC）。使用威尔科克森符号秩检验比较贝叶斯方法和oSVD方法之间的这些系数。P值
Purpose: We compared the perfoiniances of a Bayesian estimation method and oscillation index singular value decomposition (oSVD) deconvolution for predicting final infarction using data previously obtained from 10 cynomolgus monkeys with permanent unilateral middle cerebral artery (MCA) occlusion.Methods: We conducted baseline perfusion-weighted imaging 3 hours after MCA occlusion and generated time to peak, first moment of transit, cerebral blood flow, cerebral blood volume, and mean transit time maps using Bayesian and oSVD methods. Final infarct volume was determined by follow-up diffusion-weighted imaging (DWI) scanned 47 hours after MCA occlusion and from histological specimens. We used a region growing technique with various thresholds to determine perfusion abnormality volume. The best threshold was defined when the mean perfusion volume matched the mean final infarct volume, and Pearson's correlation coefficients (r) and intraclass correlations (ICC) were calculated between perfusion abnormality and final infarct volume at that threshold. These coefficients were compared between Bayesian and oSVD using Wilcoxon's signed rank test. P-value