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基于多层声速模型的合成孔径超声皮质骨成像

李云清 江晨 李颖 徐峰 许凯亮 他得安 黎仲勋

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基于多层声速模型的合成孔径超声皮质骨成像

李云清, 江晨, 李颖, 徐峰, 许凯亮, 他得安, 黎仲勋

Multi-layer velocity model based synthetic aperture ultrasound imaging of cortical bone

Li Yun-Qing, Jiang Chen, Li Ying, Xu Feng, Xu Kai-Liang, Ta De-An, Le Lawrence H.
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  • 由于皮质骨和软组织间较大的声速差异, 采用固定声速的传统超声波束形成方法无法重建皮质骨图像, 同时皮质骨中较大的衰减也限制了信号信噪比. 为了实现皮质骨超声成像, 本文提出一种采用合成孔径超声提高成像分辨率及信噪比, 利用压缩感知计算延时参数并构建多层声速模型的成像方法. 本文结合时域有限差分仿真方法分析了理想情况下皮质骨成像结果, 并结合软组织覆盖下的离体皮质骨板样本实验, 验证相关方法的可行性. 仿真和实验结果均表明, 本文方法可用于构建多层声速模型并正确重建皮质骨图像. 本研究实现了具有三层声速模型的皮质骨超声成像, 对皮质骨超声成像发展有一定的借鉴意义, 未来将进一步探索在体实验, 以推进骨超声成像的临床应用.
    With the advantages of non-ionizing and low cost, ultrasound imaging has been widely used in clinical diagnosis and treatment. However, due to the significant velocity changes between cortical bone and soft-tissue, the traditional ultrasound beamforming method under the assumption of constant velocity fails to reconstruct the cortical bone image. The velocity model based beamforming has been used in geophysics and non-destructive testing as an effective way to solve the challenges resulting from the velocity changes in multi-layer structure. Since the cortical bone can be modeled as a three-layer structure consisting of soft tissue, cortical bone and marrow, a multi-layer velocity model based synthetic aperture ultrasound method is introduced for cortical bone imaging. In this study, we first utilize synthetic transmit aperture ultrasound to obtain the full-matrix dataset to increase the signal-to-noise ratio. Second, a three-layer cortical bone velocity model is built with the compressed sensing estimated arriving time delay. The bases of compressed sensing consist of a series of excitation pulses with different delays. The received signals are regarded as a composition of the bases with different weights, thus can be projected into the bases by using compressed sensing. The time-delay of each received element is estimated by compressed sensing. According to the time-delay, the full-matrix dataset is reformed into a zero-offset format. By extracting the bases corresponded with the interface reflected signals, the time-delay between and the thickness values of the interfaces can be estimated. The velocity model can thus be built with the estimated cortical bone thickness. Based on the velocity model and zero-offset data, the phase shift migration method is used to reconstruct the cortical bone image. The finite-difference time-domain (FDTD) method is used to simulate the wave propagation in a 3.4-mm-thick cortical bone. The transmitting pulse is a Gaussian-function enveloped tone-burst signal with 6.25 MHz center frequency and 250 MHz iteration rate. The reconstructed image of simulation shows a clear top interface and bottom interface of cortical bone with correct thickness. Further FDTD simulations are carried out on a 3-mm-to-5-mm-thick cortical bone, and the average relative error of estimated thickness is 4.9% with a 13.5% variance. In vitro experiment is performed on a 3.4-mm-thick bovine bone plate to test the feasibility of the proposed method by using Verasonics platform (128-element linear array). The transmitting pulse is a Gaussian-function enveloped tone-burst signal with 6.25 MHz center frequency and 25 MHz sampling rate. The reconstructed image in experiment reveals a clear top interface and bottom interface of cortical bone with correct thickness. The experiment is repeated several times and the average relative error of estimated thickness is 3.6% with a 5.4% variance. The results of simulation and experiment both indicate that compressed sensing is effective in estimating the delay parameters of the velocity model. Finally, we evaluate the capability of compressed sensing in time-delay estimation, and the result shows that compressed sensing is more accurate than Hilbert transform even in a 20 dB-noise condition. In conclusion, the proposed method can be useful in the thickness estimation and the ultrasound imaging of cortical bone. In vivo experiment and clinical application should be further investigated.
      通信作者: 许凯亮, xukl@fudan.edu.cn ; 他得安, tda@fudan.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 11525416, 11827808, 11804056)、上海市科委国际合作项目(批准号: 17510710700)、上海市自然科学基金(批准号: 19ZR1402700)和专用集成电路与系统国家重点实验室自主课题(批准号: 2018MS004)资助的课题
      Corresponding author: Xu Kai-Liang, xukl@fudan.edu.cn ; Ta De-An, tda@fudan.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11525416, 11827808, 11804056), the Shanghai Committee of Science and Technology International Cooperation Project, China (Grant No. 17510710700), the Natural Science Foundation of Shanghai, China (Grant No. 19ZR1402700), and the State Key Laboratory for ASIC and System, China (Grant No. 2018MS004)
    [1]

    Bover J, Bailone L, Lopez-Baez V, Benito S, Ciceri P, Galassi A, Cozzolino M 2017 J. Nephrol. 30 677Google Scholar

    [2]

    章轶立, 魏戌, 申浩, 谢雁鸣 2018 中国骨质疏松杂志 24 676Google Scholar

    Zhang Y L, Wei X, Shen H, Xie Y M 2018 Chin. J. Osteoporos. 24 676Google Scholar

    [3]

    Oo W M, Naganathan V, Bo M T, Hunter D J 2018 Quant. Imaging. Med. Surg. 8 100Google Scholar

    [4]

    他得安, 王威琪, 汪源源 2009 应用声学 28 161Google Scholar

    Ta D A, Wang W Q, Wang Y Y 2009 Appl. Acoust. 28 161Google Scholar

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    Minonzio J G, Bochud N, Vallet Q, Ramiandrisoa D, Etcheto A, Briot K, Kolta S, Roux C, Laugier P 2019 J. Bone Miner. Res.Google Scholar

    [6]

    Ta D A, Wang W Q, Wang Y Y, Le L H, Zhou Y Q 2009 Ultrasound. Med. Biol. 35 641Google Scholar

    [7]

    Xu K L, Minonzio J G, Ta D A, Hu B, Wang W Q, Laugier P 2016 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 63 1514Google Scholar

    [8]

    Xu K L, Ta D A, He R X, Qin Y X, Wang W Q 2014 Ultrasound. Med. Biol. 40 817Google Scholar

    [9]

    Bai L, Xu K L, Li D, Ta D A, Le L H, Wang W Q 2018 J. Biomech. 77 83Google Scholar

    [10]

    Xu K L, Laugier P, Minonzio J G 2018 J. Acoust. Soc. Am. 143 2729Google Scholar

    [11]

    Chartier L B, Bosco L, Lapointe-Shaw L, Chenkin J 2017 CJEM 19 131Google Scholar

    [12]

    刘洋, 郭霞生, 章东, 龚秀芬 2011 声学学报 36 179Google Scholar

    Liu Y, Guo X S, Zhang D, Gong X F 2011 Acta Acustica 36 179Google Scholar

    [13]

    Li H J, Le L H, Sacchi M D, Lou E H M 2013 Ultrasound. Med. Biol. 39 1482Google Scholar

    [14]

    Renaud G, Kruizinga P, Cassereau D, Laugier P 2018 Phys. Med. Biol. 63 125010Google Scholar

    [15]

    Jensen J A, Nikolov S I, Gammelmark K L, Pedersen M H 2006 Ultrasonics 44 e5Google Scholar

    [16]

    Yu M Y, Li Y, Ma T, Shung K K, Zhou Q F 2017 IEEE Trans. Med. Imaging 36 2171Google Scholar

    [17]

    Nayak R, Schifitto G, Doyley M M 2017 Med. Phys. 44 4068Google Scholar

    [18]

    Brandt A H, Hemmsen M C, Hansen P M, Madsen S S, Krohn P S, Lange T, Hansen K L, Jensen J A, Nielsen M B 2015 Ultrasound. Med. Biol. 41 2368Google Scholar

    [19]

    Taner M T, Koehler F 1969 Geophysics 34 821

    [20]

    Saenger E H, Kocur G K, Jud R, Torrilhon M 2011 Appl. Math. Modell. 35 807Google Scholar

    [21]

    Gazdag J 1978 Geophysics 43 1342Google Scholar

    [22]

    Olofsson T 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 2522Google Scholar

    [23]

    孙宝申, 沈建中 1993 应用声学 3 43Google Scholar

    Sun B S, Shen J Z 1993 Appl. Acoust. 3 43Google Scholar

    [24]

    Trots I, Nowicki A, Lewandowski M 2010 WASET 4 136

    [25]

    康荣宗, 田鹏武, 于宏毅 2014 物理学报 63 200701Google Scholar

    Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701Google Scholar

    [26]

    刘珍黎, 宋亮华, 白亮, 许凯亮, 他得安 2017 物理学报 66 154303Google Scholar

    Liu Z L, Song L H, Bai L, Xu K L, Ta D A 2017 Acta Phys. Sin. 66 154303Google Scholar

    [27]

    刘成成 2014 博士学位论文 (上海: 复旦大学)

    Liu C C 2014 Ph. D. Dissertation (Shanghai: Fudan University) (in Chinese)

    [28]

    Baron C, Talmant M, Laugier P 2007 J. Acoust. Soc. Am. 122 1810Google Scholar

    [29]

    Pithioux M, Lasaygues P, Chabrand P 2002 J. Biomech. 35 961Google Scholar

    [30]

    Qin K H, Yang C, Sun F 2014 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61 133Google Scholar

  • 图 1  方法流程图

    Fig. 1.  Flow chart of the proposed method.

    图 2  合成孔径超声原理 (a)合成孔径聚焦; (b)发射合成孔径

    Fig. 2.  Principle of synthetic aperture ultrasound: (a) Synthetic aperture focusing technique; (b) synthetic transmit aperture.

    图 3  PSM模型 (a)单层介质模型; (b)多层介质模型

    Fig. 3.  PSM model: (a) Single layer model; (b) multi-layer model.

    图 4  实验装置示意图

    Fig. 4.  Experiment setup.

    图 5  仿真结果 (a)压缩感知前30个基底; (b)单通道接收信号; (c)单次发射全部通道接收信号; (d)压缩感知调整的单次发射全部通道接收信号

    Fig. 5.  Simulated results: (a) The first 30 bases of compressed sensing; (b) received signal of single element; (c) received signals of all elements; (d) compressed sensing based temporally adjusted received signals of all elements.

    图 6  仿真重建结果 (a)未建立声速模型的仿真重建结果; (b)建立声速模型的仿真重建结果

    Fig. 6.  Simulated reconstructed results: (a) Simulated reconstructed result without velocity model; (b) simulated reconstructed result with velocity model.

    图 7  实验结果 (a)压缩感知前30个基底; (b)单通道接收信号; (c)单次发射全部通道接收信号; (d)压缩感知调整的单次发射全部通道接收信号

    Fig. 7.  Experiment results: (a) The first 30 bases of compressed sensing; (b) received signal of single element; (c) received signals of all elements; (d) compressed sensing based temporally adjusted received signals of all elements.

    图 8  实验重建结果 (a)未建立声速模型的实验重建结果; (b)建立声速模型的实验重建结果

    Fig. 8.  Experiment reconstructed results: (a) Experiment reconstructed result without velocity model; (b) experiment reconstructed result with velocity model.

    图 9  压缩感知与Hilbert变换比较 (a)原始信号及带噪信号; (b)针对原始信号和带噪信号的压缩感知结果; (c)针对原始信号和带噪信号的Hilbert变换结果

    Fig. 9.  Comparison of compressed sensing and Hilbert transform: (a) Origin signal and noisy signal; (b) result of compressed sensing; (c) result of Hilbert transform.

    表 1  皮质骨厚度估计及误差

    Table 1.  Estimation and relative error of cortical bone thickness.

    参量数值
    真实厚度/mm3.03.13.23.33.43.53.63.73.83.94.04.14.24.34.44.54.64.74.84.9
    估计厚度/mm2.73.23.33.03.43.93.93.33.54.04.14.24.24.04.54.54.34.64.64.7
    相对误差/%103.23.19.10118.3117.92.62.52.406.92.306.52.14.24.1
    下载: 导出CSV
  • [1]

    Bover J, Bailone L, Lopez-Baez V, Benito S, Ciceri P, Galassi A, Cozzolino M 2017 J. Nephrol. 30 677Google Scholar

    [2]

    章轶立, 魏戌, 申浩, 谢雁鸣 2018 中国骨质疏松杂志 24 676Google Scholar

    Zhang Y L, Wei X, Shen H, Xie Y M 2018 Chin. J. Osteoporos. 24 676Google Scholar

    [3]

    Oo W M, Naganathan V, Bo M T, Hunter D J 2018 Quant. Imaging. Med. Surg. 8 100Google Scholar

    [4]

    他得安, 王威琪, 汪源源 2009 应用声学 28 161Google Scholar

    Ta D A, Wang W Q, Wang Y Y 2009 Appl. Acoust. 28 161Google Scholar

    [5]

    Minonzio J G, Bochud N, Vallet Q, Ramiandrisoa D, Etcheto A, Briot K, Kolta S, Roux C, Laugier P 2019 J. Bone Miner. Res.Google Scholar

    [6]

    Ta D A, Wang W Q, Wang Y Y, Le L H, Zhou Y Q 2009 Ultrasound. Med. Biol. 35 641Google Scholar

    [7]

    Xu K L, Minonzio J G, Ta D A, Hu B, Wang W Q, Laugier P 2016 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 63 1514Google Scholar

    [8]

    Xu K L, Ta D A, He R X, Qin Y X, Wang W Q 2014 Ultrasound. Med. Biol. 40 817Google Scholar

    [9]

    Bai L, Xu K L, Li D, Ta D A, Le L H, Wang W Q 2018 J. Biomech. 77 83Google Scholar

    [10]

    Xu K L, Laugier P, Minonzio J G 2018 J. Acoust. Soc. Am. 143 2729Google Scholar

    [11]

    Chartier L B, Bosco L, Lapointe-Shaw L, Chenkin J 2017 CJEM 19 131Google Scholar

    [12]

    刘洋, 郭霞生, 章东, 龚秀芬 2011 声学学报 36 179Google Scholar

    Liu Y, Guo X S, Zhang D, Gong X F 2011 Acta Acustica 36 179Google Scholar

    [13]

    Li H J, Le L H, Sacchi M D, Lou E H M 2013 Ultrasound. Med. Biol. 39 1482Google Scholar

    [14]

    Renaud G, Kruizinga P, Cassereau D, Laugier P 2018 Phys. Med. Biol. 63 125010Google Scholar

    [15]

    Jensen J A, Nikolov S I, Gammelmark K L, Pedersen M H 2006 Ultrasonics 44 e5Google Scholar

    [16]

    Yu M Y, Li Y, Ma T, Shung K K, Zhou Q F 2017 IEEE Trans. Med. Imaging 36 2171Google Scholar

    [17]

    Nayak R, Schifitto G, Doyley M M 2017 Med. Phys. 44 4068Google Scholar

    [18]

    Brandt A H, Hemmsen M C, Hansen P M, Madsen S S, Krohn P S, Lange T, Hansen K L, Jensen J A, Nielsen M B 2015 Ultrasound. Med. Biol. 41 2368Google Scholar

    [19]

    Taner M T, Koehler F 1969 Geophysics 34 821

    [20]

    Saenger E H, Kocur G K, Jud R, Torrilhon M 2011 Appl. Math. Modell. 35 807Google Scholar

    [21]

    Gazdag J 1978 Geophysics 43 1342Google Scholar

    [22]

    Olofsson T 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 2522Google Scholar

    [23]

    孙宝申, 沈建中 1993 应用声学 3 43Google Scholar

    Sun B S, Shen J Z 1993 Appl. Acoust. 3 43Google Scholar

    [24]

    Trots I, Nowicki A, Lewandowski M 2010 WASET 4 136

    [25]

    康荣宗, 田鹏武, 于宏毅 2014 物理学报 63 200701Google Scholar

    Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701Google Scholar

    [26]

    刘珍黎, 宋亮华, 白亮, 许凯亮, 他得安 2017 物理学报 66 154303Google Scholar

    Liu Z L, Song L H, Bai L, Xu K L, Ta D A 2017 Acta Phys. Sin. 66 154303Google Scholar

    [27]

    刘成成 2014 博士学位论文 (上海: 复旦大学)

    Liu C C 2014 Ph. D. Dissertation (Shanghai: Fudan University) (in Chinese)

    [28]

    Baron C, Talmant M, Laugier P 2007 J. Acoust. Soc. Am. 122 1810Google Scholar

    [29]

    Pithioux M, Lasaygues P, Chabrand P 2002 J. Biomech. 35 961Google Scholar

    [30]

    Qin K H, Yang C, Sun F 2014 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61 133Google Scholar

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出版历程
  • 收稿日期:  2019-05-19
  • 修回日期:  2019-07-02
  • 上网日期:  2019-09-01
  • 刊出日期:  2019-09-20

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