12B-5 Eigen-Based Clutter Filters for Color Flow Imaging: Single-Ensemble vs. Multi-Ensemble Approaches

A. Yu, L. Løvstakken
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引用次数: 7

Abstract

In designing eigen-based clutter filters for color flow imaging, one of the challenges is to develop an accurate way of estimating the eigen-components that represent clutter in slow- time ensembles. To provide new insights on the problem, this paper presents a comparative analysis on how eigen-fllters perform when using eigen-estimation methods that involve multiple ensembles or a single ensemble. The analysis consists of two parts: 1) a comparative review on the principles behind different eigen-estimation methods; 2) an eigen-flltering experiment done with coronary flow imaging data acquired from a porcine during bypass graft operation. For an imaging case containing tissue motion due to myocardial contraction, our analysis showed that the single-ensemble eigen-fllter shared similar performance with a multi-ensemble eigen-fllter that uses small (5times5) ensemble windows (with about 1 dB difference in clutter suppression level). Results also showed that a multi-ensemble eigen-fllter with large (20times20) ensemble windows yielded poorer performance (clutter suppression level was 3 to 6 dB lower).
基于特征的杂波滤波器用于彩色流成像:单集成与多集成方法
在设计用于彩色流成像的基于特征的杂波滤波器时,挑战之一是开发一种准确估计慢时间集成中代表杂波的特征分量的方法。为了对该问题提供新的见解,本文对使用涉及多个集成或单个集成的特征估计方法时特征滤波器的性能进行了比较分析。分析包括两个部分:1)比较分析了不同特征估计方法的原理;2)利用猪旁路移植术中冠状动脉血流成像数据进行特征滤波实验。对于包含心肌收缩引起的组织运动的成像情况,我们的分析表明,单系综特征滤波器与使用小(5倍5)系综窗口(杂波抑制水平相差约1 dB)的多系综特征滤波器具有相似的性能。结果还表明,大集成窗口(20倍20)的多系综特征滤波器性能较差(杂波抑制水平低3 ~ 6 dB)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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