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通过 DCE-MRI 利用节段性与主动脉动脉输入函数对腰椎灌注进行定量 Tofts 建模。

Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function.

机构信息

Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.

Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan.

出版信息

Sci Rep. 2021 Feb 3;11(1):2920. doi: 10.1038/s41598-021-82300-6.

Abstract

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters K, v, and v, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal-Wallis test with a Dunn-Bonferroni post hoc analysis were performed. In simulation, K and v increased as the peak in the AIF decreased, but v increased when delay time in the AIF increased. In humans, the estimated K and v were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

摘要

本研究旨在探讨在使用轴向动态对比增强磁共振成像(DCE-MRI)定量椎体灌注时,动脉输入函数(AIF)选择对其的影响。在这项研究中,使用 1.5T 扫描仪对 8 名健康志愿者(平均年龄 36.9 岁,5 名男性)的每 2 个椎体进行了轴向 DCE-MRI。使用 Tofts 模型评估了来自腰椎轴向 DCE-MRI 的药代动力学参数 K、v 和 v,采用了多种 AIF:基于人群的主动脉 AIF(AIF_PA)、患者特异性主动脉 AIF(AIF_A)和患者特异性节段性动脉 AIF(AIF_SA)。此外,还改变了峰值和延迟时间,以模拟各种 AIF 对灌注参数计算的影响。进行了非参数分析,包括 Wilcoxon 符号秩检验和 Kruskal-Wallis 检验,以及 Dunn-Bonferroni 事后分析。在模拟中,随着 AIF 峰值的降低,K 和 v 增加,但当 AIF 中的延迟时间增加时,v 增加。在人体中,无论计算方式(像素级或基于感兴趣区域的方式)如何,使用 AIF_A 估计的 K 和 v 明显小于 AIF_SA。与 AIF_A 相比,使用 AIF_SA 时这两个灌注参数均明显更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8f9/7859214/766482de4693/41598_2021_82300_Fig1_HTML.jpg

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