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首页> 外文会议>Conference on biomedical applications in molecular, structural, and functional imaging >Multiparametric prediction of acute ischemic stroke tissue outcome using CT perfusion datasets
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Multiparametric prediction of acute ischemic stroke tissue outcome using CT perfusion datasets

机译：使用CT灌注数据集的多参数预测急性缺血性中风组织的预后

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Acute ischemic strokes are a major cause for death and severe neurologic deficits in the western hemisphere. The prediction of tissue outcome in case of an acute ischemic stroke is an important variable for treatment decision. An estimation of the expected outcome is typically obtained by thresholding a single perfusion parameter map, which is calculated from a perfusion CT dataset. However, cerebral perfusion is complex and the severity of perfusion impairment is not consistent within the penumbra of an acute ischemic stroke. Therefore, the application of only one parameter for acute stroke tissue outcome prediction may oversimplify the given problem. The aim of this study was to develop and evaluate the feasibility of a multiparametric approach for estimating tissue outcome in acute ischemic stroke patients using 15 CT perfusion datasets. For this purpose, perfusion parameter maps of cerebral blood flow, cerebral blood volume and mean transit time were calculated based on the concentration time curves derived from perfusion CT datasets. The parameter maps of ten patients were employed for a voxel-wise training of a support vector machine using ground-truth final infarct segmentations, whereas the remaining five patient datasets were used for evaluation of the voxel-wise prediction of tissue outcome using the trained support vector machine. Furthermore, tissue outcome was also predicted by optimal thresholding of corresponding time-to-peak(TTP)maps for comparison purposes. Both predictions were compared to ground-truth final infarct lesions for the five datasets used for evaluation. The proposed multiparametric tissue outcome prediction lead to superior prediction results in all cases. More precisely, the multiparametric prediction lead to a mean Dice coefficient of 0.556, while optimal thresholding of TTP maps lead to an average Dice-coefficient of 0.444 compared to the ground-truth infarct lesions. In conclusion, the evaluation results of the proposed method suggest that a multiparametric tissue outcome prediction may be feasible for CT perfusion datasets but needs to be evaluated in more detail.
机译：急性缺血性中风是导致西半球死亡和严重神经功能缺损的主要原因。急性缺血性卒中时组织预后的预测是决定治疗的重要变量。通常通过对单个灌注参数图设定阈值来获得预期结果的估计值，该参数是根据灌注CT数据集计算得出的。然而，脑灌注是复杂的，并且在急性缺血性中风的半影内，灌注损伤的严重程度并不一致。因此，仅将一个参数用于急性中风组织结果预测可能会简化给定的问题。这项研究的目的是开发和评估使用15个CT灌注数据集估计急性缺血性中风患者组织结局的多参数方法的可行性。为此，根据从灌注CT数据集得出的浓度时间曲线，计算了脑血流量，脑血容量和平均通过时间的灌注参数图。使用地面真相最终梗塞分割，将10位患者的参数图用于支持向量机的体素训练，而其余5位患者数据集则用于使用训练有素的支持者对组织结果进行体素预测评估向量机。此外，还通过相应的峰值时间（TTP）映射的最佳阈值进行预测以预测组织结局，以进行比较。对于用于评估的五个数据集，将这两个预测与真实的最终梗死灶进行了比较。所提出的多参数组织结局预测可在所有情况下提供出色的预测结果。更准确地说，与地面真相梗塞病变相比，多参数预测得出的平均Dice系数为0.556，而TTP映射的最佳阈值导致的平均Dice系数为0.444。总之，所提出方法的评估结果表明，对于CT灌注数据集，多参数组织结局预测可能是可行的，但需要更详细地评估。

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