Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT.
Validation of parameter estimation methods for determining optical properties of atherosclerotic tissues in intravascular OCT.
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验证血管内 OCT 中确定动脉粥样硬化组织光学特性的参数估计方法。
DOI：
10.1117/12.2043654
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Wilson,DavidL
中科院分区：
文献类型：
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作者：
Shalev,Ronny;Gargesha,Madhusudhana;Prabhu,David;Tanaka,Kentaro;Rollins,AndrewM;Costa,Marco;Bezerra,HiramG;Lamouche,Guy;Wilson,DavidL
在本文中，我们提出了一种通过 3D 回拉评估组织光学特性的新流程，这是 iOCT 数据的标准临床采集方法。我们的方法分析了由大约 100 条 A 线组成的感兴趣体积 (VOI)，这些 A 线横跨旋转角 (θ) 并沿着动脉 z 分布。新的 3D 方法使用导管校正、基线去除、散斑噪声降低、A 线序列对齐和鲁棒估计。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，其中对许多图像帧进行平均以减少噪声。为了以受控方式进行这些研究，我们使用包含多种“组织类型”的真实视动脉模型。报告了 3D 回拉分析的精度和准确度。我们的结果表明，当在固定采集数据集上实施该过程时，每个阶段的不确定性都会提高，同时不确定性也会降低。当比较静止采集数据集和回拉数据集时，数值如下： 钙：静止时为 3.8±1.09mm−1，回拉时为 3.9±1.2 mm−1；脂质：静止时为 11.025±0.417 mm−1，回拉时为 11.27±0.25 mm−1；纤维状：静止时为 6.08±1.337 mm−1，静止时为 5.58±2.0 mm−1。这些结果表明，在比较静态和回拉数据集时，本文提出的过程引入了最小的偏差，并且不确定性只有很小的变化，从而为高度准确的临床斑块类型辨别铺平了道路，从而实现了自动分类。
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.