首页 > 论文 > 激光与光电子学进展 > 56卷 > 2期(pp:21701--1)

基于相干系数的实时超声内镜合成孔径成像算法

Real-Time Synthetic-Aperture Imaging Algorithm for Ultrasonic Endoscopy Based on Coherence Factor

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对医用超声内镜系统中合成孔径算法旁瓣噪声严重、时间复杂度高等问题, 基于图形处理器(GPU)的高速并行处理能力, 提出了一种基于相干系数的实时合成孔径算法。通过引入相干系数, 对超声回波图像中的旁瓣噪声进行有效抑制。通过GPU对算法进行并行处理, 提高算法的运行速度。使用仿真数据和实际暗斑回波数据进行实验验证, 结果表明, 该算法可以有效改善超声回波图像质量, 在回波图像数据量为153 Mbit时, 相较于合成孔径算法, 运算速度提升了16.29倍, 达6 frame·s-1, 满足医用超声内镜实时处理的要求。

Abstract

To ameliorate the serious sidelobe noise and high time complexity of the conventional synthetic-aperture algorithm in medical ultrasonic endoscopy systems, a coherence-factor-based real-time synthetic-aperture algorithm is proposed in this paper, which is realized with the high-speed parallel processing capabilities of a graphic processor (GPU). Introducing the coherence factor effectively suppresses sidelobe noise in an ultrasonic echo image, and the algorithm is processed using a GPU to improve its speed. The simulation data and the actual data are compared for experimental verification, and the proposed algorithm is shown to effectively improve the quality of the ultrasonic echo image. When the echo image size is 153 Mbit, the operation speed is 16.29 times faster than that obtained by using the conventional synthetic-aperture algorithm. A processing speed of 6 frame·s-1 is achieved to satisfy the requirements of real-time processing for medical ultrasonic endoscopy.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/lop56.021701

所属栏目:医用光学与生物技术

基金项目:天津市自然科学基金项目(15JCQNJC14200)

收稿日期:2018-06-15

修改稿日期:2018-06-19

网络出版日期:2018-07-30

作者单位    点击查看

肖禹泽:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
陈晓冬:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
徐勇:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
杨晋:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
盛婧:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
梁浩林:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072
汪毅:天津大学精密仪器与光电子工程学院, 光电信息技术教育部重点实验室, 天津 300072

联系人作者:陈晓冬(xdchen@tju.edu.cn)

【1】Zhang X L. Medical imaging physics course[M]. Beijing: Science Press, 2013.
张学龙. 医学影像物理学教程[M]. 北京: 科学出版社, 2013.

【2】Asl B M, Mahloojifar A. Minimum variance beam forming combined with adaptive coherence weighting applied to medical ultrasound imaging[J]. IEEE Transactions on Ultrasonics Ferroelectrics, and Frequency Control, 2009, 56(9): 1923-1931.

【3】Jensen J A, Svendsen N B. Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 1992, 39(2): 262-267.

【4】Li J K, Chen X D, Wang Y, et al. Eigenspace-based generalized sidelobe canceler beamforming applied to medical ultrasound imaging[J]. Sensors, 2016, 16(8): 1192.

【5】Li P C, Li M L. Adaptive imaging using the generalized coherence factor[J]. IEEE Transactions on Ultrasonics Ferroelectrics & Frequency Control, 2003, 50(2): 128-141.

【6】Bolz J, Farmer I, Grinspun E, et al. Sparse matrix solvers on the GPU: conjugate gradients and multigrid[C]∥Proceedings of SIGGRAPH′03, July 21-31, 2003, San Diego, California. New York: ACM, 2003, 22(3): 917-924.

【7】Li W, Chen X D, Li J K, et al. Parallel implementation of synthetic aperture imaging algorithm for endoscopic ultrasound[J]. Laser & Optoelectronics Progress, 2017, 54(10): 102001.
李溦, 陈晓冬, 李嘉科, 等. 内镜超声合成孔径成像算法的并行实现[J]. 激光与光电子学进展, 2017, 54(10): 102001.

【8】Nowicki A, Gambin B. Ultrasonic synthetic apertures[J]. Archives of Acoustics, 2014, 39(4): 427-438.

【9】Yu J L, Liu B, Wang K Z, et al. A highly efficient GPU-based signal processor of synthetic aperture radar[J]. Information and Electronic Engineering, 2010, 8(4): 415-419.
俞惊雷, 柳彬, 王开志, 等. 一种基于GPU的高效合成孔径雷达信号处理器[J]. 太赫兹科学与电子信息学报, 2010, 8(4): 415-419.

【10】Lackner B, Schmidinger G, Pieh S, et al. Repeatability and reproducibility of central corneal thickness measurement with Pentacam, Orbscan, and ultrasound[J]. Optometry and Vision Science, 2005, 82(10): 892-899.

【11】Liu B, Wang K Z, Liu X Z, et al. Imaging algorithm of synthetic aperture radar based on GPU via CUDA[J]. Information Technology, 2009(11): 62-65.
柳彬, 王开志, 刘兴钊, 等. 利用CUDA实现的基于GPU的SAR成像算法[J]. 信息技术, 2009(11): 62-65.

【12】Jensen J A. User′s guide for the Field II program[M]. Denmark: Technical University of Denmark, 2001.

【13】Zhu H L, Liu C, Zhang B, et al. Research on laser ultrasonic visual image processing[J]. Chinese Journal of Lasers, 2018, 45(1): 0104004.
朱洪玲, 刘畅, 张博, 等. 激光超声可视化图像处理研究[J]. 中国激光, 2018, 45(1): 0104004.

【14】Bian G Y, Wang Y, Bai B P, et al. Phased array imaging algorithm for endoscopic ultrasound based on coded excitation[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011103.
卞光宇, 汪毅, 白宝平, 等. 基于编码激励的内镜超声相控阵成像算法[J]. 激光与光电子学进展, 2018, 55(1): 011103.

【15】Liu S J, Fu H C, Wei K, et al. Jointly compensated imaging algorithm of inverse synthetic aperture lidar based on Nelder-Mead simplex method[J]. Acta Optica Sinica, 2018, 38(7): 0711002.
刘盛捷, 付翰初, 魏凯, 等. 基于Nelder-Mead单纯形法的逆合成孔径激光雷达联合补偿成像算法[J]. 光学学报, 2018, 38(7): 0711002.

引用该论文

Xiao Yuze,Chen Xiaodong,Xu Yong,Yang Jin,Sheng Jing,Liang Haolin,Wang Yi. Real-Time Synthetic-Aperture Imaging Algorithm for Ultrasonic Endoscopy Based on Coherence Factor[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021701

肖禹泽,陈晓冬,徐勇,杨晋,盛婧,梁浩林,汪毅. 基于相干系数的实时超声内镜合成孔径成像算法[J]. 激光与光电子学进展, 2019, 56(2): 021701

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF