University of Padova, Department of Information Engineering, Via Gradenigo 6/B, 35131 Padova, Italy.
Comput Methods Programs Biomed. 2011 Dec;104(3):e148-57. doi: 10.1016/j.cmpb.2011.02.012. Epub 2011 Apr 1.
Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.
动态磁敏感对比磁共振成像（DSC-MRI）数据分析需要动脉输入函数（AIF）的知识来量化脑血流量（CBF）、体积（CBV）和平均通过时间（MTT）。AIF 可以手动或使用自动算法获得。我们提出了一种在大脑中动脉（MCA）上获得 AIF 的方法。该算法在 MCA 所在的区域中绘制感兴趣区域（ROI）。然后，它在 ROI 上使用递归聚类分析来选择动脉体素。该算法已在模拟数据上与文献中最先进的自动算法进行了比较，并在临床数据上与手动程序进行了比较。在模拟数据中，我们的方法可以重建真实的 AIF，并且比其他方法受部分容积效应偏差的影响更小。在临床数据中，与手动 AIF 相比，自动 AIF 提供的 CBF 和 MTT 图具有更高的对比度水平。因此，使用所提出的方法获得的 AIF 提高了估计的可靠性，并提供了定量可靠的生理图像。
