The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Ultrasound Department, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China.
Phys Med Biol. 2023 Dec 22;69(1). doi: 10.1088/1361-6560/ad11a2.
. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when used in such ensembles.. To address the above challenges of imaging in short ensembles, two optimized filters of angular domain data are proposed in this paper: grouped angle SVD (GA-SVD) and angular-coherence-based higher-order SVD (AC-HOSVD). GA-SVD applies SVD to the concatenation of all angular data to improve clutter rejection performance in short ensembles, while AC-HOSVD applies HOSVD to the angular data tensor and utilizes angular coherence in addition to spatial and temporal features for filtering. Feasible threshold selection strategies in each feature space are provided. The clutter rejection performance of the proposed filters and SVD was evaluated with Doppler phantom andstudies at different cases. Moreover, the robustness of the filters was explored under wrong singular value threshold estimation, and their computational complexity was studied.. Qualitative and quantitative results indicated that GA-SVD and AC-HOSVD can effectively improve clutter rejection performance in short ensembles, especially AC-HOSVD. Notably, the proposed methods using 20 frames had similar image quality to SVD using 100 frames.studies showed that compared to SVD, GA-SVD increased the signal-to-noise-ratio (SNR) by 6.03 dB on average, and AC-HOSVD increased the SNR by 8.93 dB on average. Furthermore, AC-HOSVD remained better power Doppler image quality under non-optimal thresholds, followed by GA-SVD.. The proposed filters can greatly enhance contrast-free microvascular visualization in short ensembles and have potential for different clinical translations due to the performance differences.
无对比微血管成像在评估生理状态和疾病的早期诊断方面具有重要的临床价值。有效的杂波滤波对于无对比增强的微血管可视化至关重要。基于奇异值分解（SVD）的时空滤波器已广泛用于抑制杂波。然而，临床实时成像依赖于短序列（几十帧），这限制了 SVD 滤波的实现，因为在这种短序列中，特征相关估计的误差较大，并且对最优阈值的依赖性较高。为了解决短序列成像中的上述挑战，本文提出了两种优化的角域数据滤波器：分组角 SVD（GA-SVD）和基于角度相干性的高阶 SVD（AC-HOSVD）。GA-SVD 将 SVD 应用于所有角数据的串联，以提高短序列中的杂波抑制性能，而 AC-HOSVD 将 HOSVD 应用于角数据张量，并利用角相干性以及空间和时间特征进行滤波。提供了每个特征空间中的可行阈值选择策略。使用多普勒体模和不同案例研究评估了所提出的滤波器和 SVD 的杂波抑制性能。此外，还研究了滤波器在错误奇异值阈值估计下的鲁棒性，并研究了它们的计算复杂度。定性和定量结果表明，GA-SVD 和 AC-HOSVD 可以有效地提高短序列中的杂波抑制性能，特别是 AC-HOSVD。值得注意的是，使用 20 帧的所提出的方法与使用 100 帧的 SVD 具有相似的图像质量。研究表明，与 SVD 相比，GA-SVD 平均将信噪比（SNR）提高了 6.03dB，AC-HOSVD 平均将 SNR 提高了 8.93dB。此外，在非最优阈值下，AC-HOSVD 仍然保持更好的功率多普勒图像质量，其次是 GA-SVD。所提出的滤波器可以大大增强短序列中无对比微血管的可视化，并且由于性能差异，具有不同临床转化的潜力。
