From the Department of Radiology (Y.S., B.N.D., A.H.D., K.N.), Icahn School of Medicine at Mount Sinai, New York City, NY.
Department of Neurology (J.T.F., S.T., D.W.), Icahn School of Medicine at Mount Sinai, New York City, NY.
Stroke. 2018 Oct;49(10):2345-2352. doi: 10.1161/STROKEAHA.118.021952.
Background and Purpose- Estimation of infarction based on computed tomographic perfusion (CTP) has been challenging, mainly because of noise associated with CTP data. The Bayesian method is a robust probabilistic method that minimizes effects of oscillation, tracer delay, and noise during residue function estimation compared with other deconvolution methods. This study compares CTP-estimated ischemic core volume calculated by the Bayesian method and by the commonly used block-circulant singular value deconvolution technique. Methods- Patients were included if they had (1) anterior circulation ischemic stroke, (2) baseline CTP, (3) successful recanalization defined by thrombolysis in cerebral infarction ≥IIb, and (4) minimum infarction volume of >5 mL on follow-up magnetic resonance imaging (MRI). CTP data were processed with circulant singular value deconvolution and Bayesian methods. Two established CTP methods for estimation of ischemic core volume were applied: cerebral blood flow (CBF) method (relative CBF, <30% within the region of delay >2 seconds) and cerebral blood volume method (<2 mL per 100 g within the region of relative mean transit time >145%). Final infarct volume was determined on MRI (fluid-attenuated inversion recovery images). CTP and MRI-derived ischemic core volumes were compared by univariate and Bland-Altman analysis. Results- Among 35 patients included, the mean/median (mL) difference for CTP-estimated ischemic core volume against MRI was -4/-7 for Bayesian CBF ( P=0.770), 20/12 for Bayesian cerebral blood volume ( P=0.041), 21/10 for circulant singular value deconvolution CBF ( P=0.006), and 35/18 for circulant singular value deconvolution cerebral blood volume ( P<0.001). Among all methods, Bayesian CBF provided the narrowest limits of agreement (-28 to 19 mL) in comparison with MRI. Conclusions- Despite existing variabilities between CTP postprocessing methods, Bayesian postprocessing increases accuracy and limits variability in CTP estimation of ischemic core.
背景与目的- 基于计算机断层灌注（CTP）的梗死估计一直具有挑战性，主要是因为 CTP 数据存在噪声。与其他去卷积方法相比，贝叶斯方法是一种稳健的概率方法，可以最大程度地减少残差函数估计过程中的振荡、示踪剂延迟和噪声的影响。本研究比较了基于贝叶斯方法和常用的块循环奇异值去卷积技术计算的 CTP 估计的缺血核心体积。方法- 纳入标准为：（1）前循环缺血性脑卒中；（2）基线 CTP；（3）溶栓治疗后达到脑梗死溶栓治疗 2b 级以上的再通；（4）随访磁共振成像（MRI）上的最小梗死体积＞5ml。用循环奇异值去卷积和贝叶斯方法处理 CTP 数据。应用两种成熟的 CTP 方法来估计缺血核心体积：脑血流（CBF）法（区域延迟＞2 秒时相对 CBF＜30%）和脑血容量法（相对平均通过时间＞145%时区域内每 100g ＜2ml）。最终的梗死体积由 MRI（FLAIR 序列）确定。通过单变量和 Bland-Altman 分析比较 CTP 和 MRI 衍生的缺血核心体积。结果- 35 例患者中，贝叶斯 CBF 与 MRI 比较的 CTP 估计缺血核心体积的均值/中位数（ml）差值为-4/-7（ P=0.770），贝叶斯脑血容量差值为 20/12（ P=0.041），循环奇异值去卷积 CBF 差值为 21/10（ P=0.006），循环奇异值去卷积脑血容量差值为 35/18（ P＜0.001）。在所有方法中，与 MRI 相比，贝叶斯 CBF 的一致性限制最窄（-28 至 19ml）。结论- 尽管 CTP 后处理方法之间存在差异，但贝叶斯后处理可提高 CTP 估计缺血核心的准确性，并限制其变异性。
