IEEE Trans Med Imaging. 2018 Jul;37(7):1574-1586. doi: 10.1109/TMI.2018.2789499.
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 flow subspace 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.
基于奇异值分解的超声快速成像技术近来已经证明，与经典的傅里叶基相比，它能够构建出更适合于组织和血流区分的向量基，从而极大地提高了杂波滤波和血流估计的性能。然而，如何最优地估计组织子空间和血流子空间之间的边界问题仍然没有得到解决。在这里，我们引入了一种有效的子空间自动阈值估计器，并将其与基于奇异分量主要特征（即奇异值、时间奇异向量和空间奇异向量）的 13 种可能实现这一任务的估计器进行了比较。这 14 种估计器的性能在体外一组不同组织运动和血流速度的受控实验条件下的仿体上进行了测试。基于空间奇异向量相似度的估计器的性能优于其他所有估计器。除了解决阈值问题外，该估计器的另一个优点是其去噪能力，它可以将对比噪声比提高至少 5dB，将噪声基底降低至少 5dB。这证实了，与几乎完全基于时间特征的传统杂波滤波技术不同，高效的超声快速多普勒成像的杂波滤波不能忽视空间。最后，该估计器被应用于各种器官（人脑、肾脏、颈动脉和甲状腺）的体内研究，并表现出高效的杂波滤波和噪声抑制作用，大大提高了所获得的超声快速功率多普勒图像的动态范围。
