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80 排 CT 灌注在急性缺血性脑卒中患者中贝叶斯估计算法和奇异值分解算法的比较。

Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke.

机构信息

Department of Radiological Technology, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki, Okayama, 710-8602, Japan.

Department of Neurosurgery, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki, Okayama, 710-8602, Japan.

出版信息

Radiol Med. 2021 Jun;126(6):795-803. doi: 10.1007/s11547-020-01316-6. Epub 2021 Jan 19.

Abstract

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

目的

CT 灌注后处理算法种类繁多,在定量图谱方面存在很大差异。虽然有研究表明贝叶斯估计算法具有潜在优势,但在临床环境中,该算法与其他算法的直接比较仍然很少。本研究旨在比较贝叶斯估计算法和奇异值分解(SVD)算法在使用 80 排 CT 灌注评估急性缺血性卒中中的性能。

方法

使用 Vitrea 实施的标准 SVD 算法、重新制定的 SVD 算法和贝叶斯估计算法分析 36 例急性缺血性卒中患者的 CT 灌注数据。分析定量参数(脑血容量[CBV]、脑血流量[CBF]、平均通过时间[MTT]、达峰时间[TTP]和延迟时间)患侧与对侧之间的相关性和统计学差异。通过非参数 Passing-Bablok 回归分析评估 CT 灌注估计值与随访弥散加权成像衍生的梗死体积的一致性。

结果

贝叶斯估计算法的 CBF 和 MTT 明显不同,与标准 SVD 算法的相关性更好(ρ=0.78 和 0.80,p<0.001),而与重新制定的 SVD 算法的相关性较差(ρ=0.59 和 0.39,p<0.001)。仅使用重新制定的 SVD 算法时,MTT 没有显著差异(p=0.217)。在回归线上,贝叶斯估计算法的斜率和截距接近理想(y=2.42x-6.51;ρ=0.60,p<0.001),与 SVD 算法相比。

结论

与 SVD 算法相比,贝叶斯估计算法在评估急性缺血性卒中时性能更好,因为它可以更好地描绘异常灌注区域并准确估计梗死体积。

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