Christensen Søren, Mouridsen Kim, Wu Ona, Hjort Niels, Karstoft Henrik, Thomalla Götz, Röther Joachim, Fiehler Jens, Kucinski Thomas, Østergaard Leif
Center of Functionally Integrative Neuroscience, Department of Neuroradiology, Aarhus University Hospital, Building 30, Nørrebrogade 44, 8000 Aarhus C, Denmark.
Stroke. 2009 Jun;40(6):2055-61. doi: 10.1161/STROKEAHA.108.546069. Epub 2009 Apr 9.
Perfusion-weighted imaging can predict infarct growth in acute stroke and potentially be used to select patients with tissue at risk for reperfusion therapies. However, the lack of consensus and evidence on how to best create PWI maps that reflect tissue at risk challenges comparisons of results and acute decision-making in trials. Deconvolution using an arterial input function has been hypothesized to generate maps of a more quantitative nature and with better prognostic value than simpler summary measures such as time-to-peak or the first moment of the concentration time curve. We sought to compare 10 different perfusion parameters by their ability to predict tissue infarction in acute ischemic stroke.
In a retrospective analysis of 97 patients with acute stroke studied within 6 hours from symptom onset, we used receiver operating characteristics in a voxel-based analysis to compare 10 perfusion parameters: time-to-peak, first moment, cerebral blood volume and flow, and 6 variants of time to peak of the residue function and mean transit time maps. Subanalysis assessed the effect of reperfusion on outcome prediction.
The most predictive maps were the summary measures first moment and time-to-peak. First moment was significantly more predictive than time to peak of the residue function and local arterial input function-based methods (P<0.05), but not significantly better than conventional mean transit time maps.
Results indicated that if a single map type was to be used to predict infarction, first moment maps performed at least as well as deconvolved measures. Deconvolution decouples delay from tissue perfusion; we speculate this negatively impacts infarct prediction.
灌注加权成像可预测急性卒中的梗死灶扩大,并有潜力用于筛选适合再灌注治疗的有组织灌注风险的患者。然而,对于如何最佳创建反映有灌注风险组织的灌注加权成像图,目前缺乏共识和证据,这给试验结果的比较及急性决策带来了挑战。与诸如达峰时间或浓度-时间曲线的一阶矩等更简单的汇总指标相比,使用动脉输入函数进行去卷积被认为能生成更具定量性质且具有更好预后价值的图像。我们试图通过比较10种不同灌注参数预测急性缺血性卒中组织梗死的能力。
在一项对97例症状发作6小时内研究的急性卒中患者的回顾性分析中,我们在基于体素的分析中使用受试者工作特征曲线来比较10种灌注参数:达峰时间、一阶矩、脑血容量和脑血流量,以及残差函数达峰时间和平均通过时间图的6种变体。亚组分析评估了再灌注对预后预测的影响。
预测性最强的图像是汇总指标一阶矩和达峰时间。一阶矩的预测性显著高于基于残差函数达峰时间和局部动脉输入函数的方法(P<0.05),但并不显著优于传统的平均通过时间图。
结果表明,如果要用单一类型的图像来预测梗死,一阶矩图像的表现至少与去卷积测量法一样好。去卷积将延迟与组织灌注解耦;我们推测这对梗死预测产生了负面影响。