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