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

doi:10.7498/aps.68.20190763

摘要

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.

关键词:

Abstract

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.

Keywords:

作者及机构信息

1.
复旦大学电子工程系, 上海　200433

2.
阿尔伯塔大学放射与诊断图像系, 埃德蒙顿　T6G2B7

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

Authors and contacts

1.
Department of Electronic Engineering, Fudan University, Shanghai 200433, China

2.
Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton T6G2B7, Canada

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)

文章全文 : translate this paragraph

参考文献

[1]	Bover J, Bailone L, Lopez-Baez V, Benito S, Ciceri P, Galassi A, Cozzolino M 2017 J. Nephrol. 30 677 Google Scholar
[2]	章轶立, 魏戌, 申浩, 谢雁鸣 2018 中国骨质疏松杂志 24 676 Google Scholar Zhang Y L, Wei X, Shen H, Xie Y M 2018 Chin. J. Osteoporos. 24 676 Google Scholar
[3]	Oo W M, Naganathan V, Bo M T, Hunter D J 2018 Quant. Imaging. Med. Surg. 8 100 Google Scholar
[4]	他得安, 王威琪, 汪源源 2009 应用声学 28 161 Google Scholar Ta D A, Wang W Q, Wang Y Y 2009 Appl. Acoust. 28 161 Google 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. 35 641 Google 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 1514 Google Scholar
[8]	Xu K L, Ta D A, He R X, Qin Y X, Wang W Q 2014 Ultrasound. Med. Biol. 40 817 Google Scholar
[9]	Bai L, Xu K L, Li D, Ta D A, Le L H, Wang W Q 2018 J. Biomech. 77 83 Google Scholar
[10]	Xu K L, Laugier P, Minonzio J G 2018 J. Acoust. Soc. Am. 143 2729 Google Scholar
[11]	Chartier L B, Bosco L, Lapointe-Shaw L, Chenkin J 2017 CJEM 19 131 Google Scholar
[12]	刘洋, 郭霞生, 章东, 龚秀芬 2011 声学学报 36 179 Google Scholar Liu Y, Guo X S, Zhang D, Gong X F 2011 Acta Acustica 36 179 Google Scholar
[13]	Li H J, Le L H, Sacchi M D, Lou E H M 2013 Ultrasound. Med. Biol. 39 1482 Google Scholar
[14]	Renaud G, Kruizinga P, Cassereau D, Laugier P 2018 Phys. Med. Biol. 63 125010 Google Scholar
[15]	Jensen J A, Nikolov S I, Gammelmark K L, Pedersen M H 2006 Ultrasonics 44 e5 Google Scholar
[16]	Yu M Y, Li Y, Ma T, Shung K K, Zhou Q F 2017 IEEE Trans. Med. Imaging 36 2171 Google Scholar
[17]	Nayak R, Schifitto G, Doyley M M 2017 Med. Phys. 44 4068 Google 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 2368 Google 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. 35 807 Google Scholar
[21]	Gazdag J 1978 Geophysics 43 1342 Google Scholar
[22]	Olofsson T 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 2522 Google Scholar
[23]	孙宝申, 沈建中 1993 应用声学 3 43 Google Scholar Sun B S, Shen J Z 1993 Appl. Acoust. 3 43 Google 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 200701 Google Scholar Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701 Google Scholar
[26]	刘珍黎, 宋亮华, 白亮, 许凯亮, 他得安 2017 物理学报 66 154303 Google Scholar Liu Z L, Song L H, Bai L, Xu K L, Ta D A 2017 Acta Phys. Sin. 66 154303 Google 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 1810 Google Scholar
[29]	Pithioux M, Lasaygues P, Chabrand P 2002 J. Biomech. 35 961 Google Scholar
[30]	Qin K H, Yang C, Sun F 2014 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61 133 Google Scholar

施引文献

图 1 方法流程图

Fig. 1. Flow chart of the proposed method.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

图 4 实验装置示意图

Fig. 4. Experiment setup.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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.

下载: 全尺寸图片幻灯片

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/mm	3.0	3.1	3.2	3.3	3.4	3.5	3.6	3.7	3.8	3.9	4.0	4.1	4.2	4.3	4.4	4.5	4.6	4.7	4.8	4.9
估计厚度/mm	2.7	3.2	3.3	3.0	3.4	3.9	3.9	3.3	3.5	4.0	4.1	4.2	4.2	4.0	4.5	4.5	4.3	4.6	4.6	4.7
相对误差/%	10	3.2	3.1	9.1	0	11	8.3	11	7.9	2.6	2.5	2.4	0	6.9	2.3	0	6.5	2.1	4.2	4.1

下载: 导出CSV

[1]	Bover J, Bailone L, Lopez-Baez V, Benito S, Ciceri P, Galassi A, Cozzolino M 2017 J. Nephrol. 30 677 Google Scholar
[2]	章轶立, 魏戌, 申浩, 谢雁鸣 2018 中国骨质疏松杂志 24 676 Google Scholar Zhang Y L, Wei X, Shen H, Xie Y M 2018 Chin. J. Osteoporos. 24 676 Google Scholar
[3]	Oo W M, Naganathan V, Bo M T, Hunter D J 2018 Quant. Imaging. Med. Surg. 8 100 Google Scholar
[4]	他得安, 王威琪, 汪源源 2009 应用声学 28 161 Google Scholar Ta D A, Wang W Q, Wang Y Y 2009 Appl. Acoust. 28 161 Google 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. 35 641 Google 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 1514 Google Scholar
[8]	Xu K L, Ta D A, He R X, Qin Y X, Wang W Q 2014 Ultrasound. Med. Biol. 40 817 Google Scholar
[9]	Bai L, Xu K L, Li D, Ta D A, Le L H, Wang W Q 2018 J. Biomech. 77 83 Google Scholar
[10]	Xu K L, Laugier P, Minonzio J G 2018 J. Acoust. Soc. Am. 143 2729 Google Scholar
[11]	Chartier L B, Bosco L, Lapointe-Shaw L, Chenkin J 2017 CJEM 19 131 Google Scholar
[12]	刘洋, 郭霞生, 章东, 龚秀芬 2011 声学学报 36 179 Google Scholar Liu Y, Guo X S, Zhang D, Gong X F 2011 Acta Acustica 36 179 Google Scholar
[13]	Li H J, Le L H, Sacchi M D, Lou E H M 2013 Ultrasound. Med. Biol. 39 1482 Google Scholar
[14]	Renaud G, Kruizinga P, Cassereau D, Laugier P 2018 Phys. Med. Biol. 63 125010 Google Scholar
[15]	Jensen J A, Nikolov S I, Gammelmark K L, Pedersen M H 2006 Ultrasonics 44 e5 Google Scholar
[16]	Yu M Y, Li Y, Ma T, Shung K K, Zhou Q F 2017 IEEE Trans. Med. Imaging 36 2171 Google Scholar
[17]	Nayak R, Schifitto G, Doyley M M 2017 Med. Phys. 44 4068 Google 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 2368 Google 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. 35 807 Google Scholar
[21]	Gazdag J 1978 Geophysics 43 1342 Google Scholar
[22]	Olofsson T 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 2522 Google Scholar
[23]	孙宝申, 沈建中 1993 应用声学 3 43 Google Scholar Sun B S, Shen J Z 1993 Appl. Acoust. 3 43 Google 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 200701 Google Scholar Kang R Z, Tian P W, Yu H Y 2014 Acta Phys. Sin. 63 200701 Google Scholar
[26]	刘珍黎, 宋亮华, 白亮, 许凯亮, 他得安 2017 物理学报 66 154303 Google Scholar Liu Z L, Song L H, Bai L, Xu K L, Ta D A 2017 Acta Phys. Sin. 66 154303 Google 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 1810 Google Scholar
[29]	Pithioux M, Lasaygues P, Chabrand P 2002 J. Biomech. 35 961 Google Scholar
[30]	Qin K H, Yang C, Sun F 2014 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61 133 Google Scholar

[1]	王攀, 王仲根, 孙玉发, 聂文艳. 新型压缩感知计算模型分析三维电大目标电磁散射特性. 物理学报, 2023, 72(3): 030202. doi: 10.7498/aps.72.20221532
[2]	胡耀华, 刘艳, 穆鸽, 秦齐, 谭中伟, 王目光, 延凤平. 基于多模光纤散斑的压缩感知在光学图像加密中的应用. 物理学报, 2020, 69(3): 034203. doi: 10.7498/aps.69.20191143
[3]	陈炜, 郭媛, 敬世伟. 基于深度学习压缩感知与复合混沌系统的通用图像加密算法. 物理学报, 2020, 69(24): 240502. doi: 10.7498/aps.69.20201019
[4]	康志伟, 吴春艳, 刘劲, 马辛, 桂明臻. 基于两级压缩感知的脉冲星时延估计方法. 物理学报, 2018, 67(9): 099701. doi: 10.7498/aps.67.20172100
[5]	冷雪冬, 王大鸣, 巴斌, 王建辉. 基于渐进添边的准循环压缩感知时延估计算法. 物理学报, 2017, 66(9): 090703. doi: 10.7498/aps.66.090703
[6]	王盼盼, 姚旭日, 刘雪峰, 俞文凯, 邱棚, 翟光杰. 基于行扫描测量的运动目标压缩成像. 物理学报, 2017, 66(1): 014201. doi: 10.7498/aps.66.014201
[7]	李少东, 陈永彬, 刘润华, 马晓岩. 基于压缩感知的窄带高速自旋目标超分辨成像物理机理分析. 物理学报, 2017, 66(3): 038401. doi: 10.7498/aps.66.038401
[8]	李慧, 赵琳, 李亮. 基于贝叶斯压缩感知的周跳探测与修复方法. 物理学报, 2016, 65(24): 249101. doi: 10.7498/aps.65.249101
[9]	时洁, 杨德森, 时胜国, 胡博, 朱中锐. 基于压缩感知的矢量阵聚焦定位方法. 物理学报, 2016, 65(2): 024302. doi: 10.7498/aps.65.024302
[10]	庄佳衍, 陈钱, 何伟基, 冒添逸. 基于压缩感知的动态散射成像. 物理学报, 2016, 65(4): 040501. doi: 10.7498/aps.65.040501
[11]	李广明, 吕善翔. 混沌信号的压缩感知去噪. 物理学报, 2015, 64(16): 160502. doi: 10.7498/aps.64.160502
[12]	仲亚军, 刘娇, 梁文强, 赵生妹. 针对多散斑图的差分压缩鬼成像方案研究. 物理学报, 2015, 64(1): 014202. doi: 10.7498/aps.64.014202
[13]	康荣宗, 田鹏武, 于宏毅. 一种基于选择性测量的自适应压缩感知方法. 物理学报, 2014, 63(20): 200701. doi: 10.7498/aps.63.200701
[14]	陈明生, 王时文, 马韬, 吴先良. 基于压缩感知的目标频空电磁散射特性快速分析. 物理学报, 2014, 63(17): 170301. doi: 10.7498/aps.63.170301
[15]	张新鹏, 胡茑庆, 程哲, 钟华. 基于压缩感知的振动数据修复方法. 物理学报, 2014, 63(20): 200506. doi: 10.7498/aps.63.200506
[16]	王哲, 王秉中. 压缩感知理论在矩量法中的应用. 物理学报, 2014, 63(12): 120202. doi: 10.7498/aps.63.120202
[17]	李龙珍, 姚旭日, 刘雪峰, 俞文凯, 翟光杰. 基于压缩感知超分辨鬼成像. 物理学报, 2014, 63(22): 224201. doi: 10.7498/aps.63.224201
[18]	宁方立, 何碧静, 韦娟. 基于lp范数的压缩感知图像重建算法研究. 物理学报, 2013, 62(17): 174212. doi: 10.7498/aps.62.174212
[19]	冯丙辰, 方晟, 张立国, 李红, 童节娟, 李文茜. 基于压缩感知理论的非线性γ谱分析方法. 物理学报, 2013, 62(11): 112901. doi: 10.7498/aps.62.112901
[20]	白旭, 李永强, 赵生妹. 基于压缩感知的差分关联成像方案研究. 物理学报, 2013, 62(4): 044209. doi: 10.7498/aps.62.044209

计量

文章访问数: 9770
PDF下载量: 139
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

搜索

留言板