Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke
Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke
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贝叶斯估计算法与奇异值分解算法在急性缺血性脑卒中患者 80 排 CT 灌注中的比较
DOI：
10.1007/s11547-020-01316-6
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
T. Morita
中科院分区：
文献类型：
--
作者：
S. Ichikawa;Hiroyuki Yamamoto;T. Morita
CT灌注的后处理算法多种多样，但在定量图方面存在很大差异。尽管提出了贝叶斯估计算法的潜在优势，但在临床环境中与其他算法的直接比较仍然很少。我们的目的是比较贝叶斯估计算法和奇异值分解（SVD）算法在使用80个检测器行CT灌注评估急性缺血性卒中中的性能。采用Vitrea实现标准SVD算法、改进型SVD算法和贝叶斯估计算法对36例急性缺血性脑卒中患者的CT灌注数据进行分析。分析患侧与对侧定量参数（脑血容量[CBV]、脑血流量[CBF]、平均传递时间[MTT]、到达峰值时间[TTP]、延迟时间）的相关性及统计学差异。采用非参数passingbablok回归分析评估CT灌注估计和随访弥散加权成像衍生梗死体积的一致性。贝叶斯估计算法的CBF和MTT存在很大差异，与标准SVD算法的相关性（ρ = 0.78和0.80,p < 0.001）优于与重新制定的SVD算法的相关性（ρ = 0.59和0.39,p < 0.001）。仅当使用重新制定的SVD算法时，MTT无显著差异（p = 0.217）。对于回归线，与奇异值分解算法相比，贝叶斯估计算法的斜率和截距接近理想（y = 2.42 x-6.51; ρ = 0.60, p < 0.001）。与SVD算法相比，贝叶斯估计算法能更好地描绘异常灌注区域，准确估计梗死面积，在急性缺血性脑卒中的评估中具有更好的性能。
A variety of postprocessing algorithms for CT perfusion are available, with substantial differences in terms of quantitative maps. Although potential advantages of a Bayesian estimation algorithm are suggested, direct comparison with other algorithms in clinical settings remains scarce. We aimed to compare performance of a Bayesian estimation algorithm and singular value decomposition (SVD) algorithms for the assessment of acute ischemic stroke using an 80-detector row CT perfusion. CT perfusion data of 36 patients with acute ischemic stroke were analyzed using the Vitrea implemented a standard SVD algorithm, a reformulated SVD algorithm and a Bayesian estimation algorithm. Correlations and statistical differences between affected and contralateral sides of quantitative parameters (cerebral blood volume [CBV], cerebral blood flow [CBF], mean transit time [MTT], time to peak [TTP] and delay) were analyzed. Agreement of the CT perfusion-estimated and the follow-up diffusion-weighted imaging-derived infarct volume were evaluated by nonparametric Passing–Bablok regression analysis. CBF and MTT of the Bayesian estimation algorithm were substantially different and showed a better correlation with the standard SVD algorithm (ρ = 0.78 and 0.80, p < 0.001) than with the reformulated SVD algorithm (ρ = 0.59 and 0.39, p < 0.001). There is no significant difference in MTT only when using the reformulated SVD algorithm (p = 0.217). Regarding the regression lines, the slope and intercept were nearly ideal with the Bayesian estimation algorithm (y = 2.42 x-6.51; ρ = 0.60, p < 0.001) in comparison with the SVD algorithms. The Bayesian estimation algorithm can lead to a better performance compared with the SVD algorithms in the assessment of acute ischemic stroke because of better delineation of abnormal perfusion areas and accurate estimation of infarct volume.
影响因子：
19.7
作者：
Kudo, Kohsuke;Sasaki, Makoto;Ogasawara, Kuniaki
通讯作者：
Ogasawara, Kuniaki
影响因子：
19.7
作者：
Kudo, Kohsuke;Sasaki, Makoto;Shirato, Hiroki
通讯作者：
Shirato, Hiroki