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Microvascular Flow Estimation by Contrast-Assisted Ultrasound B-Scan and Statistical Parametric Images

机译:造影剂超声B扫描和统计参数图像的微血管流量估计

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摘要

The microbubbles destruction/replenishment technique has been previously applied to estimating blood flow in the microcirculation. The rate of increase of the time-intensity curve (TIC) due to microbubbles flowing into the region of interest (ROI), as measured from B-mode images, closely reflects the flow velocity. In previous studies, we proposed a new approach called the time-Nakagami-parameter curve (TNC) obtained from Nakagami images to monitor microbubble replenishment for quantifying the microvascular flow velocity. This study aimed to further explore some effects that may affect the TNC to estimate the microflow, including microbubble concentration, ultrasound transmitting energy, attenuation, intrinsic noise, and tissue clutter. In order to well control each effect production, we applied a typical simulation method to investigate the TIC and TNC. The rates of increase of the TIC and TNC were expressed by the rate constants betaI and betaN, respectively, of a monoexponential model. The results show that betaN quantifies the microvascular flow velocity similarly to the conventional betaI. Moreover, the measures of betaI and betaN are not influenced by microbubble concentration, transducer excitation energy, and attenuation effect. Although the effect of intrinsic signals contributed by noise and blood would influence the TNC behavior, the TNC method has a better tolerance of tissue clutter than the TIC does, allowing the presence of some clutter components in the ROI. The results suggest that the TNC method can be used as a complementary tool for the conventional TIC to reduce the wall filter requirements for blood flow measurement in the microcirculation.
机译:先前已经将微泡破坏/补充技术应用于估计微循环中的血流。从B模式图像测量,由于微气泡流入关注区域(ROI)而导致的时间强度曲线(TIC)的增加速率紧密反映了流速。在以前的研究中,我们提出了一种新的方法,称为“时间-中上参数曲线(TNC)”,该方法是从中上图像中获取的,以监控微气泡补给以量化微血管流速。这项研究旨在进一步探讨可能影响TNC估计微流的一些影响,包括微气泡浓度,超声传输能量,衰减,固有噪声和组织杂波。为了很好地控制每种效果的产生,我们采用了一种典型的仿真方法来研究TIC和TNC。 TIC和TNC的增加速率分别由单指数模型的速率常数betaI和betaN表示。结果表明,betaN与常规betaI相似,可量化微血管流速。而且,betaI和betaN的量度不受微气泡浓度,换能器激发能和衰减效应的影响。尽管噪声和血液引起的固有信号的影响会影响TNC的行为,但TNC方法比TIC具有更好的组织杂波耐受性,从而使ROI中存在一些杂波成分。结果表明,TNC方法可用作常规TIC的补充工具,以减少微循环中血流测量所需的壁式过滤器。

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