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Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT

机译:确定血管内OCT中动脉粥样硬化组织的光学特性的参数估计方法的验证

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摘要

In this paper we present a new process for assessing optical properties of tissues from 3D pullbacks, the standard clinical acquisition method for iOCT data. Our method analyzes a volume of interest (VOI) consisting of about 100 A-lines spread across the angle of rotation (θ) and along the artery, z. The new 3D method uses catheter correction, baseline removal, speckle noise reduction, alignment of A-line sequences, and robust estimation. We compare results to those from a more standard, “gold standard” stationary acquisition where many image frames are averaged to reduce noise. To do these studies in a controlled fashion, we use a realistic optical artery phantom containing of multiple “tissue types.” Precision and accuracy for 3D pullback analysis are reported.Our results indicate that when implementing the process on a stationary acquisition dataset, the uncertainty improves at each stage while the uncertainty is reduced. When comparing stationary acquisition dataset to pullback dataset, the values were as follows: calcium: 3.8±1.09mm−1 in stationary and 3.9±1.2 mm−1 in a pullback; lipid: 11.025±0.417 mm−1 in stationary and 11.27±0.25 mm−1 in pullback; fibrous: 6.08±1.337 mm−1 in stationary and 5.58±2.0 mm−1. These results indicates that the process presented in this paper introduce minimal bias and only a small change in uncertainty when comparing a stationary and pullback dataset, thus paves the way to a highly accurate clinical plaque type discrimination, enabling automatic classification.
机译:在本文中,我们提出了一种从3D后撤评估组织光学特性的新方法,这是iOCT数据的标准临床采集方法。我们的方法分析感兴趣的体积(VOI),该体积由大约100条A线组成,这些A线分布在旋转角(θ)上并沿着动脉z分布。新的3D方法使用导管校正,基线去除,斑点噪声减少,A线序列比对和可靠估计。我们将结果与更标准的“黄金标准”静态采集的结果进行比较,在这种采集中,平均许多图像帧以减少噪声。为了以受控的方式进行这些研究,我们使用了包含多种“组织类型”的逼真的光学动脉体模。报告了3D回撤分析的精度和准确性。我们的结果表明,在固定采集数据集上实施该过程时,不确定性在每个阶段都会提高,而不确定性会降低。当将静态采集数据集与回撤数据集进行比较时,值如下:钙:静态时为3.8±1.09mm -1 ,而回撤时为3.9±1.2mm -1 。脂质:静止时为11.025±0.417 mm -1 ,回缩时为11.27±0.25 mm -1 ;纤维:静止时为6.08±1.337 mm -1 ,而5.58±2.0 mm -1 。这些结果表明,在比较固定数据集和回撤数据集时,本文介绍的过程引入了最小的偏差,不确定性仅发生了很小的变化,从而为高精度的临床菌斑类型判别铺平了道路,从而实现了自动分类。

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