Closed-Loop Low-Rank Echocardiographic Artifact Removal.
Closed-Loop Low-Rank Echocardiographic Artifact Removal.
复制标题
闭环式低阶超声心动图伪影去除。
DOI：
10.1109/tuffc.2020.3013268
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Hossack JA
中科院分区：
文献类型：
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作者：
Govinahallisathyanarayana S;Acton ST;Hossack JA
超声心动图图像序列经常被叠加在运动心肌上的准静态伪影（“杂波”）所破坏。传统的利用杂波中的局部相关性的局部盲源分离方法在抑制这些伪影方面是有效的。这些方法利用光谱特征来区分组织杂波和背景噪声，并在数据集上进行详尽的应用。穷尽式应用导致计算复杂度高，丢失有用的组织信号。在本文中，我们开发了一种闭环算法，该算法首先使用自适应确定的加权函数检测杂波，然后使用低秩估计方法去除杂波。通过提出主成分域稀疏编码和核范数最小化两种低秩估计，证明了该方法适用于不同的低秩估计。我们将我们提出的方法（CLEAR）与奇异值滤波（SVF）和形态成分分析（MCA）两种方法的性能进行了比较。通过测量相对于已知的“地面真相”数据集的误差，对移动杂波和组织的不同组合进行量化。与基准方法8.5±0.7 dB （SVF）和9.3±0.5 dB （MCA）相比，我们的方法保留了更多的组织，误差更低，分别为3.88±0.093 dB（稀疏编码）和3.47±0.78（核范数），特别是在组织运动和伪影运动速率较小（每帧中心频率≤0.25周期）的情况下，同时产生了相当的杂波抑制性能。CLEAR还通过在5个具有合成杂波的小鼠心脏数据集上量化心脏周期的跟踪误差，在体内进行了验证。CLEAR将误差减少了大约50%，而SVF则减少了25%。
Echocardiographic image sequences are frequently corrupted by quasi-static artifacts (“clutter”) superimposed on the moving myocardium. Conventionally, localized blind source separation methods exploiting local correlation in the clutter have proven effective in the suppression of these artifacts. These methods use spectral characteristics to distinguish clutter from tissue and background noise, and are applied exhaustively over the dataset. The exhaustive application results in high computational complexity and a loss of useful tissue signal. In this paper, we develop a closed loop algorithm in which the clutter is first detected using an adaptively determined weighting function and then removed using low rank estimation methods. We show that our method is adaptable to different low rank estimators, by presenting two such estimators: sparse coding in the principal component domain and nuclear norm minimization. We compare the performance of our proposed method (CLEAR) to two methods: singular value filtering (SVF) and morphological component analysis (MCA). The performance was quantified in silico by measuring the error with respect to a known ‘ground truth’ dataset with no clutter for different combinations of moving clutter and tissue. Our method retains more tissue with a lower error of 3.88 ± 0.093 dB (sparse coding), 3.47 ±0.78 (nuclear norm) compared to the benchmark methods 8.5 ± 0.7 dB (SVF), and 9.3 ± 0.5 dB (MCA) particularly in instances where the rate of tissue motion and artifact motion are small (≤ 0.25 periods of center frequency per frame) while producing comparable clutter reduction performance. CLEAR was also validated in vivo by quantifying tracking error over the cardiac cycle on five mouse heart datasets with synthetic clutter. CLEAR reduced the error by approximately 50%, compared to 25% for the SVF.