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首页> 外文期刊>Bioengineering >Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing
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Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing

机译:使用图像处理技术从多普勒超声心动图中提取峰值速度曲线

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The objective of this study is to extract positive and negative peak velocity profiles from Doppler echocardiographic images. These profiles are currently estimated using tedious manual approaches. Profiles can be used to establish realistic boundary conditions for computational hemodynamic studies and to estimate cardiac time intervals, which are of clinical utility. In the current study, digital image processing algorithms that rely on intensity calculations and two different thresholding methods were proposed and tested. Image intensity histograms were used to guide threshold choices, which were selected such that the resulting velocity profiles appropriately represent Doppler shift envelopes. The resulting peak velocity profiles contained artifacts in the form of sudden velocity changes and possible outliers. To reduce these artifacts, image smoothing using a moving average process was then implemented. Bland–Altman analysis suggested good agreement between the two thresholding methods. Artifacts decreased when image smoothing was performed. Results also suggested that one thresholding method tended to provide the lower limit (i.e., underestimate) of velocities, while the second tended to provide the velocity upper limit (i.e., overestimate). Combining estimates from both methods appeared to provide a smoother peak velocity profile estimate. The proposed automated approach may be useful for objective estimation of peak velocity profiles, which may be helpful for computational hemodynamic studies and may increase the efficiency of current clinical diagnostic tools.
机译:这项研究的目的是从多普勒超声心动图图像中提取正和负峰值速度曲线。这些配置文件目前使用繁琐的手动方法进行估算。轮廓可用于为计算血流动力学研究建立现实的边界条件,并估计心脏时间间​​隔,这在临床上具有实用性。在当前的研究中,提出并测试了依赖强度计算和两种不同阈值化方法的数字图像处理算法。图像强度直方图用于指导阈值选择,选择阈值以使所得的速度轮廓适当地表示多普勒频移包络。所得的峰值速度曲线包含速度突然变化和可能的异常值形式的伪影。为了减少这些伪像,然后使用移动平均过程实现了图像平滑。 Bland–Altman分析表明,两种阈值方法之间具有良好的一致性。进行图像平滑处理时,伪像减少。结果还表明,一种阈值方法倾向于提供速度的下限(即,低估),而第二种阈值方法倾向于提供速度的上限(即高估)。结合两种方法的估计值似乎可以提供更平滑的峰值速度曲线估计值。提出的自动化方法可能有助于峰值速度曲线的客观估计,这可能有助于计算血流动力学研究,并可能提高当前临床诊断工具的效率。

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