Huang Yizhou, van Sloun Ruud, Mischi Massimo
Lab. of Biomedical Diagnostics, Eindhoven University of Technology, Eindhoven, The Netherlands.
Lab. of Biomedical Diagnostics, Eindhoven University of Technology, Eindhoven, The Netherlands.
Comput Methods Programs Biomed. 2025 Mar;260:108542. doi: 10.1016/j.cmpb.2024.108542. Epub 2024 Dec 5.
The integration of ultrafast Doppler imaging with singular value decomposition clutter filtering has demonstrated notable enhancements in flow measurement and Doppler sensitivity, surpassing conventional Doppler techniques. However, in the context of transthoracic coronary flow imaging, additional challenges arise due to factors such as the utilization of unfocused diverging waves, constraints in spatial and temporal resolution for achieving deep penetration, and rapid tissue motion. These challenges pose difficulties for ultrafast Doppler imaging and singular value decomposition in determining optimal tissue-blood (TB) and blood-noise (BN) thresholds, thereby limiting their ability to deliver high-contrast Doppler images.
This study introduces a novel local blood subspace detection method that utilizes multilevel thresholding by the valley-emphasized Otsu's method to estimate the TB and BN thresholds on a pixel-based level, operating under the assumption that the magnitude of the spatial singular vector curve of each pixel resembles the shape of a trimodal Gaussian. Upon obtaining the local TB and BN thresholds, a weighted mask (WM) is generated to assess the blood content in each pixel. To enhance the computational efficiency of this pixel-based algorithm, a dedicated tree-structure k-means clustering approach, further enhanced by noise rejection (NR) at each singular vector order, is proposed to group pixels with similar spatial singular vector curves, subsequently applying local thresholding (LT) on a cluster-based (CB) level.
The effectiveness of the proposed method was evaluated using an ex-vivo setup featuring a Langendorff swine heart. Comparative analysis with power Doppler images filtered using the conventional global thresholding method, which uniformly applies TB and BN thresholds to all pixels, revealed noteworthy enhancements. Specifically, our proposed CBLT+NR+WM approach demonstrated an average 10.8-dB and 11.2-dB increase in Contrast-to-Noise ratio and Contrast in suppressing the tissue signal, paralleled by an average 5-dB (Contrast-to-Noise ratio) and 9-dB (Contrast) increase in suppressing the noise signal.
These results clearly indicate the capability of our method to attenuate residual tissue and noise signals compared to the global thresholding method, suggesting its promising utility in challenging transthoracic settings for coronary flow measurement.
将超快多普勒成像与奇异值分解杂波滤波相结合，已证明在血流测量和多普勒灵敏度方面有显著提高，超越了传统多普勒技术。然而，在经胸冠状动脉血流成像的背景下，由于诸如使用非聚焦发散波、实现深度穿透时空间和时间分辨率的限制以及组织快速运动等因素，会出现额外的挑战。这些挑战给超快多普勒成像和奇异值分解在确定最佳组织 - 血液（TB）和血液 - 噪声（BN）阈值方面带来困难，从而限制了它们提供高对比度多普勒图像的能力。
本研究引入了一种新颖的局部血液子空间检测方法，该方法利用谷值强调的大津法进行多级阈值处理，在基于像素的层面上估计TB和BN阈值，其操作假设是每个像素的空间奇异向量曲线的幅度类似于三峰高斯形状。在获得局部TB和BN阈值后，生成加权掩码（WM）以评估每个像素中的血液含量。为提高这种基于像素的算法的计算效率，提出了一种专用的树形结构k均值聚类方法，通过在每个奇异向量阶次处进行噪声抑制（NR）进一步增强，以对具有相似空间奇异向量曲线的像素进行分组，随后在基于聚类（CB）的层面上应用局部阈值处理（LT）。
使用具有Langendorff猪心脏的体外设置评估了所提出方法的有效性。与使用传统全局阈值处理方法滤波的功率多普勒图像进行比较分析，传统方法对所有像素统一应用TB和BN阈值，结果显示有显著增强。具体而言，我们提出的CBLT + NR + WM方法在抑制组织信号方面，对比度噪声比平均提高了10.8 dB，对比度平均提高了11.2 dB，同时在抑制噪声信号方面，对比度噪声比平均提高了5 dB，对比度平均提高了9 dB。
这些结果清楚地表明，与全局阈值处理方法相比，我们的方法能够减弱残余组织和噪声信号，表明其在具有挑战性的经胸冠状动脉血流测量设置中具有广阔的应用前景。
