一种基于Casorati-奇异值分解的超快平面波超声多普勒自适应时空域杂波抑制算法

徐依雯; 杨晨; 徐杰; 焦阳; 崔崤峣

doi:10.11999/JEIT200618

一种基于Casorati-奇异值分解的超快平面波超声多普勒自适应时空域杂波抑制算法

doi: 10.11999/JEIT200618

徐依雯¹,
杨晨^{2, 3},
徐杰^{2, 4},
焦阳^2, ,,
崔崤峣²

1.
苏州国科昂卓医疗科技有限公司苏州 215163
2.
中国科学院苏州生物医学工程技术研究所苏州 215163
3.
中国科学技术大学电信学院合肥 230031
4.
复旦大学工程与应用技术研究院上海 200433

基金项目: 国家自然科学基金(51805529)，江苏省重点研发计划(BE2017601, BE2017661)

详细信息

作者简介:
徐依雯：女，1993年生，硕士，研究方向为医用超声图像处理

杨晨：男，1993年生，博士生，研究方向为医用超声图像处理

徐杰：男，1990年生，副研究员，研究方向为超声系统设计、电子电路分析与设计

焦阳：男，1988年生，副研究员，研究方向为医学超声成像和系统设计

崔崤峣：女，1974年生，研究员，博士生导师，研究方向为超声成像、超声生物效应和光声效应

通讯作者:
焦阳　jiaoy@sibet.ac.cn

中图分类号: TN911.7; R445.1
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出版历程
- 收稿日期: 2020-07-24
- 修回日期: 2021-03-19
- 网络出版日期: 2021-04-13
- 刊出日期: 2021-08-10

Adaptive Spatiotemporal Clutter Rejection Based on Casorati-Singular Value Decompositionfor Ultrafast Plane-wave Doppler Imaging

Yiwen XU¹,
Chen YANG^{2, 3},
Jie XU^{2, 4},
Yang JIAO^{2
, ,},
Yaoyao CUI²

1.
Suzhou GuoKe Ultra Medical Technology Co. Ltd, Suzhou 215163, China
2.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
3.
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230031, China
4.
Academy for Engineering and Technology, Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (51805529); Jiangsu Provincial Key Research and Development Plan (BE2017601, BE2017661)

摘要

摘要: 超快超声平面波成像技术实现了超声的高帧频大视野同步采集，捕捉到更多有效原始信息，而传统滤波器在处理这种大视野数据方面有诸多不足。该文基于Casorati奇异值分解(Casorati-SVD)技术提出一种改进的自适应杂波抑制算法：首先，选取一个区域的原始平面波数据构建Casorati数据矩阵并进行奇异值分解；其次，根据分解后分量的多普勒频率和能量自适应匹配合适的滤波截止参数，抑制组织杂波和噪声并提取血流信号；最后，对每个区域重复前面的步骤并统计所有输出获取最终图像。该文分别在仿体、人体手臂动脉和家兔脑血流的回波信号上验证该算法抑制杂波的能力，这些实验结果表明，相比全局Casorati奇异值分解滤波器，这种改进的分区域自适应滤波算法将信噪比(SNR)提高4.4%～50%，对比信噪比(CNR)提高4.7%～55.9%。该技术实现了多普勒血流成像的空间自适应滤波，对临床血流成像的发展有重要意义。
- 超快超声平面波 /
- 功率多普勒 /
- Casorati奇异值分解 /
- 自适应杂波抑制
Abstract: By using compounded plane wave, it enables the high-frame-rate acquisition of synchronous ultrasonic samples in the all field of view. However, classical clutter filters fail to deal with these big synchronous imaging datasets. In this study, an improved adaptive clutter rejection algorithm based on Casorati Singular Value Decomposition (Casorati-SVD) is proposed to take full advantage of synchronous datasets. The first step is to construct a Casorati matrix based on a block of plane-wave data and perform singular value decomposition on this Casorati matrix. Then the key point is to adaptively determine the cufoff thresholds according to Doppler frequency and energy of component signals and the blood flow signal is extracted through auto-generated filter. Finally, adaptive SVD filtering on each block is performed and the final flow signals are reconstructed from all blocks. To assess its ability in noise suppression, the proposed method is applied to blood flow echos obtained from phantom, arm artery and rabbit brain. These results demonstrate the improved method has 4.4% to 50% higher Signal-to-Noise-Ratio (SNR) and 4.7% to 55.9% Contrast-to-Noise-Ratio (CNR) than conventional Casorati-SVD methods. In conclusion, this method realizes spatial adaptive filtering and can be significant for development of clinical blood flow imaging.
- Ultrafast ultrasound plane wave /
- Power Doppler /
- Casorati Singular Value Decomposition (SVD) /
- Adaptive clutter suppression

HTML全文

图 1 理想高通滤波器示意图

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图 2 相邻25个像素点慢时信号相关矩阵

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图 3 Casorati数据矩阵重构

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图 4 SVD奇异值分布示意图

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图 5 时间奇异向量频谱图

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图 6 分区域滤波方法示意图

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图 7 仿体实验与功率多普勒图像

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图 8 手臂动脉实验与功率多普勒图像

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图 9 家兔大脑功率多普勒图像

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图 10 信噪比曲线

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参考文献(28)

[1]	BERCOFF J, MONTALDO G, LOUPAS T, et al. Ultrafast compound doppler imaging: Providing full blood flow characterization[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2011, 58(1): 134–147. doi: 10.1109/TUFFC.2011.1780
[2]	尹华国, 何兴无, 周洪林. 基于CUDA的超声脉冲多普勒成像[J]. 计算机工程与应用, 2012, 48(19): 140–144. doi: 10.3778/j.issn.1002-8331.2012.19.033 YIN Huaguo, HE Xingwu, and ZHOU Honglin. Ultrasound pulsed wave doppler based on CUDA[J]. Computer Engineering and Applications, 2012, 48(19): 140–144. doi: 10.3778/j.issn.1002-8331.2012.19.033
[3]	沈志远. 超声彩色血流成像中血流信号提取方法研究[D]. [博士论文], 哈尔滨工业大学, 2014. SHEN Zhiyuan. Blood flow signal extraction methd in ultrasound color flow imaging[D]. [Ph. D. dissertation], Harbin Institute of Technology, 2014.
[4]	BJAERUM S, TORP H, and KRISTOFFERSEN K. Clutter filter design for ultrasound color flow imaging[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2002, 49(2): 204–216. doi: 10.1109/58.985705
[5]	BJAERUM S, TORP H, and KRISTOFFERSEN K. Clutter filters adapted to tissue motion in ultrasound color flow imaging[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2002, 49(6): 693–704. doi: 10.1109/TUFFC.2002.1009328
[6]	肖磊, 熊秀娟, 陈菲, 等. 超声血流成像中基于动态域的回归和奇异值分解的杂波抑制方法[J]. 计算机应用, 2015, 35(1): 265–269, 275. doi: 10.11772/j.issn.1001-9081.2015.01.0265 XIAO Lei, XIONG Xiujuan, CHEN Fei, et al. Clutter suppression method based on dynamic region regression and singular value decomposition in ultrasound flow image[J]. Journal of Computer Applications, 2015, 35(1): 265–269, 275. doi: 10.11772/j.issn.1001-9081.2015.01.0265
[7]	肖磊. 彩色超声多普勒血流成像关键技术的研究[D]. [硕士论文], 西南科技大学, 2015. XIAO Lei. Color Doppler flow imaging study of key technologies[D]. [Master dissertation], Southwest University of Science and Technology, 2016.
[8]	YOU Wei and WANG Yuanyuan. Adaptive clutter rejection for ultrasound color flow imaging based on recursive eigendecomposition[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2009, 56(10): 2217–2231. doi: 10.1109/TUFFC.2009.1304
[9]	YOO Y M and KIM Y. New adaptive clutter rejection for ultrasound color Doppler imaging: In vivo study[J]. Ultrasound in Medicine and Biology, 2010, 36(3): 480–487. doi: 10.1016/j.ultrasmedbio.2009.11.008
[10]	MACE E, MONTALDO G, OSMANSKI B F, et al. Functional ultrasound imaging of the brain: Theory and basic principles[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2013, 60(3): 492–506. doi: 10.1109/TUFFC.2013.2592
[11]	DEMENÉ C, DEFFIEUX T, PERNOT M, et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fUltrasound sensitivity[J]. IEEE Transactions on Medical Imaging, 2015, 34(11): 2271–2285. doi: 10.1109/TMI.2015.2428634
[12]	YU A C H and LOVSTAKKEN L. Eigen-based clutter filter design for ultrasound color flow imaging: A review[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(5): 1096–1111. doi: 10.1109/TUFFC.2010.1521
[13]	CHEUNG D K H, CHIU H C T, ZHANG Lequan, et al. Adaptive clutter filter design for micro-ultrasound color flow imaging of small blood vessels[C]. 2010 IEEE International Ultrasonics Symposium, San Diego, USA, 2010: 1206–1209.
[14]	王录涛, 王微, 金钢. 基于Hankel-SVD的非平稳超声血流成像杂波抑制技术研究[J]. 电子与信息学报, 2015, 37(4): 830–835. doi: 10.11999/JEIT140893 WANG Lutao, WANG Wei, and JIN Gang. Non-stationary clutter rejection based on Hankel-SVD for ultrasound color flow imaging[J]. Journal of Electronics &Information Technology, 2015, 37(4): 830–835. doi: 10.11999/JEIT140893
[15]	王录涛, 吴锡, 金钢, 等. 一种基于奇异值谱加权的超声彩色多普勒成像杂波抑制算法[J]. 电子学报, 2016, 44(6): 1294–1299. doi: 10.3969/j.issn.0372-2112.2016.06.005 WANG Lutao, WU Xi, JIN Gang, et al. A singuiar-spectrai-weighting-based ciutter rejection method for coior uitrasound doppier I maging[J]. Acta Electronica Sinica, 2016, 44(6): 1294–1299. doi: 10.3969/j.issn.0372-2112.2016.06.005
[16]	叶为镪, 郭宁, 王丛知, 等. 基于超声平面波的功率多普勒成像方法研究[J]. 集成技术, 2015, 4(3): 79–85. YE Weiqiang, GUO Ning, WANG Congzhi, et al. Study of power doppler imaging method with ultrasonic plane wave[J]. Journal of Integration Technology, 2015, 4(3): 79–85.
[17]	TANTER M and FINK M. Ultrafast imaging in biomedical ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2014, 61(1): 102–119. doi: 10.1109/TUFFC.2014.2882
[18]	ERRICO C, PIERRE J, PEZET S, et al. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging[J]. Nature, 2015, 527(7579): 499–502. doi: 10.1038/nature16066
[19]	尉明望. 超快速超声成像方法研究及其CUDA实现[D]. [硕士论文], 哈尔滨工业大学, 2016. WEI Mingwang. The research of ultrafast ultrasound imaging method and its implementation on CUDA[D]. [Master dissertation], Harbin Institute of Technology, 2016.
[20]	DEFFIEUX T, DEMENE C, PERNOT M, et al. Functional ultrasound neuroimaging: A review of the preclinical and clinical state of the art[J]. Current Opinion in Neurobiology, 2018, 50: 128–135. doi: 10.1016/j.conb.2018.02.001
[21]	HINGOT V, ERRICO C, HEILES B, et al. Microvascular flow dictates the compromise between spatial resolution and acquisition time in Ultrasound Localization Microscopy[J]. Scientific Reports, 2019, 9(1): 2456. doi: 10.1038/s41598-018-38349-x
[22]	CORREIA M, MARESCA D, GOUDOT G, et al. Quantitative imaging of coronary flows using 3D ultrafast Doppler coronary angiography[J]. Physics in Medicine & Biology, 2020, 65(10): 105013.
[23]	HINGOT V, BRODIN C, LEBRUN F, et al. Early Ultrafast Ultrasound Imaging of Cerebral Perfusion correlates with Ischemic Stroke outcomes and responses to treatment in Mice[J]. Theranostics, 2020, 10(17): 7480–7491. doi: 10.7150/thno.44233
[24]	MARESCA D, PAYEN T, LEE-GOSSELIN A, et al. Acoustic biomolecules enhance hemodynamic functional ultrasound imaging of neural activity[J]. NeuroImage, 2020, 209: 116467. doi: 10.1016/j.neuroimage.2019.116467
[25]	RAHAL L, THIBAUT M, RIVALS I, et al. Ultrafast ultrasound imaging pattern analysis reveals distinctive dynamic brain states and potent sub-network alterations in arthritic animals[J]. Scientific Reports, 2020, 10(1): 10485. doi: 10.1038/s41598-020-66967-x
[26]	SONG Pengfei, MANDUCA A, TRZASKO J D, et al. Ultrasound small vessel imaging with block-wise adaptive local clutter filtering[J]. IEEE Transactions on Medical Imaging, 2017, 36(1): 251–262. doi: 10.1109/TMI.2016.2605819
[27]	ARNAL B, BARANGER J, DEMENE C, et al. In vivo real-time cavitation imaging in moving organs[J]. Physics in Medicine & Biology, 2017, 62(3): 843–857.
[28]	BARANGER J, ARNAL B, PERREN F, et al. Adaptive spatiotemporal SVD clutter filtering for Ultrafast Doppler Imaging using similarity of spatial singular vectors[J]. IEEE Transactions on Medical Imaging, 2018, 37(7): 1574–1586. doi: 10.1109/TMI.2018.2789499