Molloi S Y, Mistretta C A
Department of Medical Physics and Radiology, University of Wisconsin-Madison 53792.
Med Phys. 1988 May-Jun;15(3):289-97. doi: 10.1118/1.596277.
Previous attempts to use time subtraction intravenous digital subtraction angiography for ventricular imaging have been hampered by artifacts due to cardiac and respiratory motion. We have previously reported a motion-immune dual-energy technique in which kVp is switched between 60 and 120, at 300-500 mA, 30 times/s. In order to quantitate parameters such as ejection fraction and left ventricular volume, it is necessary to correct for scatter and veiling glare (SVG), which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). In this report, a convolution filtering method has been investigated to estimate SVG in DSA images. In the first step, a grey level transformation of the detected image is utilized to get an estimated SVG image. In the second step this image is convolved to produce an image with appropriate spatial frequency content. Estimates of SVG in several Humanoid chest phantom images were obtained using Gaussian convolution kernels with a full width at half-maximum (FWHM) of 51-125 pixels. The root-mean-square (rms) percentage error of these estimates was obtained by comparison with direct SVG measurement. A convolution kernel with a FWHM of 75 pixels in each dimension applied to 16 Humanoid phantom images with various projections, thicknesses, and beam energies resulted in an average rms percentage error of 9.7% in the SVG estimate, for the 16 cases studied. The SVG estimation consisting of grey scale-to-SVG fraction lookup table (LUT) is made based on previous measurements. The x-ray settings required for each patient are utilized to alter the LUT in order to account for patient thickness variations.(ABSTRACT TRUNCATED AT 250 WORDS)
以往尝试使用时间减影静脉数字减影血管造影术进行心室成像时，因心脏和呼吸运动产生的伪影而受到阻碍。我们之前报道过一种运动免疫双能技术，其中千伏峰值（kVp）在60至120之间切换，电流为300 - 500毫安，每秒30次。为了定量诸如射血分数和左心室容积等参数，有必要校正散射和蒙片眩光（SVG），这是视频密度测定数字减影血管造影术（DSA）中非线性的主要来源。在本报告中，研究了一种卷积滤波方法来估计DSA图像中的SVG。第一步，利用检测图像的灰度变换得到估计的SVG图像。第二步，对该图像进行卷积以产生具有适当空间频率内容的图像。使用半高宽（FWHM）为51 - 125像素的高斯卷积核获得了几幅人形胸部体模图像中SVG的估计值。通过与直接SVG测量值比较获得这些估计值的均方根（rms）百分比误差。将每个维度FWHM为75像素的卷积核应用于16幅具有不同投影、厚度和束能量的人形体模图像，在所研究的16个病例中，SVG估计的平均rms百分比误差为9.7%。基于先前的测量制作了由灰度到SVG分数查找表（LUT）组成的SVG估计。利用每个患者所需的x射线设置来改变LUT，以考虑患者厚度的变化。（摘要截断于250字）
