Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors
Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors
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DOI：
10.1109/tmi.2018.2789499
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发表时间：
2018-07-01
影响因子：
10.6
通讯作者：
Demene, Charlie
中科院分区：
文献类型：
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作者：
Baranger, Jerome;Arnal, Bastien;Demene, Charlie
最近，超快成像超声数据集的奇异值分解已被证明可以建立比经典傅立叶基更适合组织和血流辨别的矢量基，并通过大因子杂波过滤和血流估计进行改进。然而，最佳估计组织子空间和血流子空间之间的边界的问题仍然没有得到解答。在这里，我们引入了一种用于子空间自动阈值化的有效估计器，并将其与十三个估计器的详尽列表进行比较，这些估计器可以根据奇异分量的主要特征（即奇异值、时间奇异向量和空间奇异向量）来实现此任务。这十四个估计器的性能在大量受控实验条件下进行了体外测试，模型上有不同的组织运动和流速。基于空间奇异向量相似程度的估计器优于所有其他估计器。除了解决阈值问题外，该估计器的额外好处是其去噪功能，可大幅提高对比度与噪声比并将本底噪声降低至少 5 dB。这证实，与几乎完全基于时间特性的传统杂波滤波技术相反，超快多普勒成像的高效杂波滤波不能忽视空间。最后，该估计器应用于体内各种器官（人脑、肾脏、颈动脉和甲状腺），并表现出有效的杂波过滤和噪声抑制，大大提高了所获得的超快功率多普勒图像的动态范围。
Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been shown to build a vector basis far more adapted to the discrimination of tissue and blood flow than the classical Fourier basis, improving by large factor clutter filtering and blood flow estimation. However, the question of optimally estimating the boundary between the tissue subspace and the blood flowsubspace remained unanswered. Here, we introduce an efficient estimator for automatic thresholding of subspaces and compare it to an exhaustive list of thirteen estimators that could achieve this task based on the main characteristics of the singular components, namely the singular values, the temporal singular vectors, and the spatial singular vectors. The performance of those fourteen estimators was tested in vitro in a large set of controlled experimental conditions with different tissue motion and flow speeds on a phantom. The estimator based on the degree of resemblance of spatial singular vectors outperformed all others. Apart from solving the thresholding problem, the additional benefit with this estimator was its denoising capabilities, strongly increasing the contrast to noise ratio and lowering the noise floor by at least 5 dB. This confirms that, contrary to conventional clutter filtering techniques that are almost exclusively based on temporal characteristics, efficient clutter filtering of ultrafast Doppler imaging cannot overlook space. Finally, this estimator was applied in vivo on various organs (human brain, kidney, carotid, and thyroid) and showed efficient clutter filtering and noise suppression, improving largely the dynamic range of the obtained ultrafast power Doppler images.