Pinter Stephen Z, Lacefield James C
Biomedical Engineering Graduate Program, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B9, Canada.
Ultrasound Med Biol. 2009 Jul;35(7):1217-28. doi: 10.1016/j.ultrasmedbio.2009.01.010. Epub 2009 Apr 25.
Power Doppler imaging of physiologic and pathologic angiogenesis is widely used in preclinical studies to track normal development, disease progression and treatment efficacy but can be challenging given the presence of small blood vessels and slow flow velocities. Power Doppler images can be plagued with false-positive color pixels or undetected vessels, thereby complicating the interpretation of vascularity metrics such as color pixel density (CPD). As an initial step toward improved microvascular quantification, flow-phantom experiments were performed to establish relationships between vessel detection and various combinations of vessel size (160, 200, 250, 300 and 360 microm), flow velocity (4, 3, 2, 1 and 0.5 mm/s) and transducer frequency (30 and 40 MHz) while varying the wall filter cut-off velocity. Receiver operating characteristic (ROC) curves and areas under ROC curves indicate that good vessel detection performance can be achieved with a 40-MHz transducer for flow velocities > or =2 mm/s and with a 30-MHz transducer for flow velocities > or =1 mm/s. In the second part of the analysis, CPD was plotted as a function of wall filter cut-off velocity for each flow-phantom data set. Three distinct regions were observed: overestimation of CPD at low cut-offs, underestimation of CPD at high cut-offs and a plateau at intermediate cut-offs. The CPD at the plateau closely matched the phantom's vascular volume fraction and the length of the plateau corresponded with the flow-detection performance of the Doppler system assessed using ROC analysis. Color pixel density vs. wall filter cut-off curves from analogous in vivo experiments exhibited the same shape, including a distinct CPD plateau. The similar shape of the flow-phantom and in vivo curves suggests that the presence of a plateau in vivo can be used to identify the best-estimate CPD value that can be treated as a quantitative vascularity metric. The ability to identify the best CPD estimate is expected to improve quantification of angiogenesis and anti-vascular treatment responses with power Doppler.
生理和病理血管生成的能量多普勒成像在临床前研究中被广泛用于追踪正常发育、疾病进展和治疗效果，但鉴于存在小血管和缓慢的血流速度，这可能具有挑战性。能量多普勒图像可能会受到假阳性彩色像素或未检测到的血管的困扰，从而使诸如彩色像素密度（CPD）等血管性指标的解释变得复杂。作为改进微血管定量的第一步，进行了血流模拟实验，以建立血管检测与血管大小（160、200、250、300和360微米）、血流速度（4、3、2、1和0.5毫米/秒）以及换能器频率（30和40兆赫）的各种组合之间的关系，同时改变壁滤波器截止速度。受试者工作特征（ROC）曲线和ROC曲线下面积表明，对于流速≥2毫米/秒，使用40兆赫的换能器可实现良好的血管检测性能；对于流速≥1毫米/秒，使用30兆赫的换能器可实现良好的血管检测性能。在分析的第二部分中，针对每个血流模拟数据集，将CPD绘制为壁滤波器截止速度的函数。观察到三个不同的区域：低截止值时CPD高估、高截止值时CPD低估以及中间截止值时的平稳期。平稳期的CPD与模拟物的血管体积分数密切匹配，平稳期的长度与使用ROC分析评估的多普勒系统的血流检测性能相对应。来自类似体内实验的彩色像素密度与壁滤波器截止曲线呈现相同的形状，包括明显的CPD平稳期。血流模拟曲线和体内曲线的相似形状表明，体内平稳期的存在可用于确定可被视为定量血管性指标的最佳估计CPD值。识别最佳CPD估计值的能力有望改善能量多普勒对血管生成和抗血管治疗反应的定量。
