首页> 美国卫生研究院文献>Journal of Cerebral Blood Flow Metabolism >Susceptibility of Tmax to tracer delay on perfusion analysis: quantitative evaluation of various deconvolution algorithms using digital phantoms
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Susceptibility of Tmax to tracer delay on perfusion analysis: quantitative evaluation of various deconvolution algorithms using digital phantoms

机译:Tmax对灌注分析中示踪剂延迟的敏感性:使用数字体模对各种解卷积算法的定量评估

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摘要

The time-to-maximum of the tissue residue function (Tmax) perfusion index has proven very predictive of infarct growth in large clinical trials, yet its dependency on simple tracer delays remains unknown. Here, we determine the dependency of computed tomography (CT) perfusion (CTP) Tmax estimates on tracer delay using a range of deconvolution techniques and digital phantoms. Digital phantom data sets simulating the tracer delay were created from CTP data of six healthy individuals, in which time frames of the left cerebral hemisphere were shifted forward and backward by up to ±5 seconds. These phantoms were postprocessed with three common singular value decomposition (SVD) deconvolution algorithms—standard SVD (sSVD), block-circulant SVD (bSVD), and delay-corrected SVD (dSVD)—with an arterial input function (AIF) obtained from the right middle cerebral artery (MCA). The Tmax values of the left hemisphere were compared among different tracer delays and algorithms by a region of interest-based analysis. The Tmax values by sSVD were positively correlated with ‘positive shifts' but unchanged with ‘negative shifts,' those by bSVD had an excellent positive linear correlation with both positive and negative shifts, and those by dSVD were relatively constant, although slightly increased with the positive shifts. The Tmax is a parameter highly dependent on tracer delays and deconvolution algorithm.
机译:在大型临床试验中,最大残留时间(Tmax)灌注指数已被证明可以很好地预测梗死的发生,但其对简单示踪剂延迟的依赖性仍然未知。在这里,我们使用一系列反卷积技术和数字体模确定计算机断层扫描(CT)灌注(CTP)Tmax估计值对示踪剂延迟的依赖性。由六个健康个体的CTP数据创建了模拟示踪剂延迟的数字幻象数据集,其中左脑半球的时间范围前后移动了最多±5秒。这些幻影经过三种常见的奇异值分解(SVD)反卷积算法(标准SVD(sSVD),块循环SVD(bSVD)和延迟校正的SVD(dSVD))进行后处理,并具有从动脉获得的动脉输入函数(AIF)。右大脑中动脉(MCA)。通过基于兴趣区域的分析,比较了不同示踪剂延迟和算法之间左半球的Tmax值。 sSVD的Tmax值与“正向偏移”呈正相关,但与“负向偏移”无相关性,bSVD的Tmax值与正向和负向偏移均具有极好的正线性相关性,而dSVD的Tmax值则相对恒定,尽管随着积极的转变。 Tmax是高度依赖于跟踪器延迟和反卷积算法的参数。

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