Takagi Satoshi, Tokumitsu Hideyuki, Sanada Shigeru
Radiological Center, National Defense Medical College Hospital, 3-2, Namiki, Tokorozawa, Saitama, 359-8513, Japan,
J Digit Imaging. 2015 Jun;28(3):368-72. doi: 10.1007/s10278-014-9745-5.
Volume rendering (VR) is a technique commonly used for the reconstruction of three-dimensional (3D) digital subtraction angiography (DSA) images, and the rendering parameters greatly affect the characteristics of the 3D image. This study aimed to test whether the optimal VR parameters for 3D DSA could be estimated from the contrast effects in rotational two-dimensional (2D) DSA images acquired using 3D DSA. Simulated blood vessels filled with various concentrations of contrast medium were scanned, and the 3D DSA data sets were reconstructed. The syngo AX vessel analysis software that was able to analyze 3D DSA VR image was used for objective measures. Raw data projection images of the 3D DSA data sets in which the mean diameter was calculated as a true value by the software at nine different thresholds for vessel segmentation were selected. In each image set, five images of all 133 rotational 2D DSA images were selected, and the contrast-enhanced area was extracted using a region-growing algorithm. Mean values and standard deviations of each contrast-enhanced area were calculated, and as the thresholds for vessel segmentation of the software increased by 500 every time, significant differences were observed in the mean values (P < 0.01). This optimal threshold can be applied to the window settings of the VR technique. Therefore, the optimal VR parameters for 3D DSA may be determined by analyzing the contrast effects of the raw data projection images, and user-dependent over- and underestimations of 3D DSA VR images also may be prevented.
容积再现(VR)是一种常用于三维(3D)数字减影血管造影(DSA)图像重建的技术,且渲染参数对三维图像的特征有很大影响。本研究旨在测试能否从使用3D DSA采集的旋转二维(2D)DSA图像中的对比效果来估计3D DSA的最佳VR参数。对填充有不同浓度造影剂的模拟血管进行扫描,并重建3D DSA数据集。使用能够分析3D DSA VR图像的syngo AX血管分析软件进行客观测量。选择3D DSA数据集的原始数据投影图像,该软件在九个不同的血管分割阈值下将平均直径计算为真值。在每个图像集中,从所有133张旋转二维DSA图像中选择五张图像,并使用区域生长算法提取对比增强区域。计算每个对比增强区域的平均值和标准差,并且随着软件的血管分割阈值每次增加500,平均值出现显著差异(P < 0.01)。这个最佳阈值可应用于VR技术的窗口设置。因此,3D DSA的最佳VR参数可以通过分析原始数据投影图像的对比效果来确定,并且还可以防止用户对3D DSA VR图像的过度和低估。