最小方差波束形成与广义相干系数融合的医学超声成像方法

为了在医学超声成像过程中提高分辨率并修正由生物组织的声速不均匀性引入的聚焦误差,提出了将最小波束形成与广义相干系数融合的高分辨率成像方法.首先对阵元数据进行延时聚焦处理,得到了聚焦后的多通道数据;然后对多通道数据进行最小方差波束形成,同时把阵元数据变换到波束域,计算广义相干系数;最后利用广义相干系数加权最小波束形成的结...     

特征空间和符号相干系数融合的最小方差超声波束形成

为了提高医学超声成像的空间分辨率,提出一种融合了特征空间最小方差与符号相干系数的波束形成方法。首先利用最小方差法计算回波数据的协方差矩阵和加权向量;然后对协方差矩阵进行特征分解得到信号子空间,并将加权向量投影到该空间上;最后计算符号相干系数,用于优化特征空间法得到的回波信号,最终获得超声成像数据。为验证算法的有效性,对医学超声成像中常用的点目标、斑目标进行仿真,对点目标仿体和人体颈动脉组织进行超声成像实验。结果表明:所提出的方法在分辨率、对比度以及稳健性等方面都优于传统的延时叠加算法、最小方差算法、特征空间最小方差法以及特征空间与相干系数融合的方法。     

特征空间和符号相干系数融合的最小方差超声波束形成

为了提高医学超声成像的空间分辨率,提出一种融合了特征空间最小方差与符号相干系数的波束形成方法。首先利用最小方差法计算回波数据的协方差矩阵和加权向量;然后对协方差矩阵进行特征分解得到信号子空间,并将加权向量投影到该空间上;最后计算符号相干系数,用于优化特征空间法得到的回波信号,最终获得超声成像数据。为验证算法的有效性,对医学超声成像中常用的点目标、斑目标进行仿真,对点目标仿体和人体颈动脉组织进行超声成像实验。结果表明:所提出的方法在分辨率、对比度以及稳健性等方面都优于传统的延时叠加算法、最小方差算法、特征空间最小方差法以及特征空间与相干系数融合的方法。     

超声成像中基于特征空间的前后向最小方差波束形成

针对最小方差(MV)波束形成在算法稳健性和超声成像对比度方面存在的缺点,提出一种将特征空间法和前后向空间平滑法融合用于最小方差波束形成的超声成像方法。首先用前后向空间平滑取代传统的前向空间平滑,得到更精确的协方差矩阵;然后计算最优加权向量,并将该向量投影到由协方差矩阵特征空间构造的信号子空间中;最后利用投影所获得的向量与阵元数据进行运算得到成像回波数据。为了验证算法的有效性,对医学成像中常用的点目标和斑目标进行了成像实验。仿真结果表明:所提出的方法不依赖于对角加载参数的选取,在保持MV算法高分辨率的同时,还有效提高图像的对比度和算法的稳健性。     

特征空间最小方差波束形成与相关系数特征值加权相融合的超声成像算法

为了进一步提高超声成像的质量,提出一种信号特征空间的最小方差波束形成与相关系数特征值加权相融合的超声成像算法。利用超声回波信号具有一定的相关性,而相关系数空间最大特征值可以反映回波信号相关性较强的性质,将该特征值作为自适应加权系数对信号特征空间最小方差波束形成(EIBMV)的结果进行加权成像,得到高质量的成像结果。通过对散射点目标和斑目标的Field II仿真,结果表明该算法相对于EIBMV算法,亮斑对比度提高了4.22 dB,暗斑对比度提高了1.88 dB,并且进一步提高了横向分辨率。     

基于空间广义相干系数加权的医学合成孔径超声成像

为解决广义相干系数用于合成孔径成像中所存在的运算量大,图像对比度改善有限等问题,提出空间广义相干系数加权成像方法。该算法根据单个孔径成像结果之间的相干性来计算相干系数,通过加权空间合成进行成像。采用对FieldⅡ仿真点目标和吸声斑目标的数据进行成像表明,算法不仅使运算量减少N(N为阵元数)倍,而且相对于传统的广义相干系数算法,算法对散射点成像可提高信噪比7 dB,对于吸声斑成像可提高对比度3.2 dB。可见算法可以有效地提高成像速度和成像质量。     

改进的最小方差算法在超声成像中的应用

为了提高最小方差超声成像算法的分辨率、对比度以及对噪声的鲁棒性,提出一种改进的最小方差成像算法。该方法首先基于回波信号中期望信号与噪声信号的可分离性将信号划分为期望信号和噪声信号,然后根据最小方差原理,求出加权向量使期望信号功率最小,同时,为了增加算法对噪声的鲁棒性,对信号方向向量增加一对约束条件,进一步提高图像质量。在全发全收和合成孔径模式下对点目标和吸声斑进行仿真,结果表明所提算法在全发全收模式下,-6 dB处分辨率在最小方差基础上提高了1倍左右,在合成孔径模式下,对比度在特征空间最小方差算法基础上提高了8 dB,且远优于传统延时叠加算法。最后通过实验进一步表明改进的最小方差算法图像在分辨率、对比度及对噪声的鲁棒性等方面表现更优,可以有效的改善超声图像的质量。     

近似窄带假设下的最小方差无失真响应波束形成

最小方差无失真响应波束形成算法在应用于语音等宽带信号时,依赖窄带假设可以在频域各个子带分别进行滤波。窄带假设下语音信号协方差矩阵是秩-1矩阵,而实际中窄带信号模型只是实际信号模型的一种近似,同时由于存在统计量估计误差,估计的语音信号协方差矩阵的秩一般大于1。提出利用语音协方差矩阵和噪声协方差矩阵的广义主特征向量来估计相对传递函数,用于重构语音信号协方差矩阵为秩-1矩阵。在REVERB数据集以及CHiME-4数据集上进行实验验证,最小方差无失真响应波束形成算法经过语音协方差矩阵低秩近似后,对估计误差的鲁棒性提高,输出信噪比分别提升平均0.8 dB和1.4 dB,同时提升了语音识别准确率。     

稳健的子带子阵级导向最小方差波束形成算法

导向最小方差(STMV)波束形成是一种利用导向协方差矩阵获得自适应权值的方法,具有快速收敛特性.常规的稳健导向最小方差(RSTMV)波束形成算法在处理宽频带信号时,性能下降明显.为了改善算法的性能,结合频域子带划分和空域子阵划分技术,提出一种多子带不确定集独立约束的稳健子阵级STMV波束形成算法。通过频域子带划分可对不同子带的导向向量误差范数边界进行约束,计算出各子带对应的对角加载量,得到稳健的子带级最小方差波束形成算法权向量;同时采用子阵技术进行降维处理,可进一步增加划分子带的数目,从而提高算法的性能并有效降低计算复杂度,最终得到一种稳健的子带子阵级STMV波束形成算法。理论分析和仿真结果表明,在阵列导向向量存在误差的情况下,该算法在干扰方向形成的零陷最深,且零陷波束宽度最窄,输出信噪比接近理论值,因此性能最佳.实际海试数据处理表明,在强干扰目标存在时,弱目标输出信干噪比较RSTMV算法可提高4 dB,较常规波束形成可提高10 dB,在角度分辨力和算法复杂度方面得到有效改善,同时可以保证目标功率无失真输出。     

Eigenspace-based minimum variance beamformer combined with Wiener postfilter for medical ultrasound imaging

In this paper, a new beamformer which combines the eigenspace-based minimum variance (ESBMV) beamformer with the Wiener postfilter is proposed for medical ultrasound imaging. The primary goal of this work is to further improve the medical ultrasound imaging quality on the basis of the ESBMV beamformer. In this method, we optimize the ESBMV weights with a Wiener postfilter. With the optimization of the Wiener postfilter, the output power of the new beamformer becomes closer to the actual signal power at the imaging point than the ESBMV beamformer. Different from the ordinary Wiener postfilter, the output signal and noise power needed in calculating the Wiener postfilter are estimated respectively by the orthogonal signal subspace and noise subspace constructed from the eigenstructure of the sample covariance matrix.We demonstrate the performance of the new beamformer when resolving point scatterers and cyst phantom using both simulated data and experimental data and compare it with the delay-and-sum (DAS), the minimum variance (MV) and the ESBMV beamformer. We use the full width at half maximum (FWHM) and the peak-side-lobe level (PSL) to quantify the performance of imaging resolution and the contrast ratio (CR) to quantify the performance of imaging contrast. The FWHM of the new beamformer is only 15%, 50% and 50% of those of the DAS, MV and ESBMV beamformer, while the PSL is 127.2 dB, 115 dB and 60 dB lower. What is more, an improvement of 239.8%, 232.5% and 32.9% in CR using simulated data and an improvement of 814%, 1410.7% and 86.7% in CR using experimental data are achieved compared to the DAS, MV and ESBMV beamformer respectively. In addition, the effect of the sound speed error is investigated by artificially overestimating the speed used in calculating the propagation delay and the results show that the new beamformer provides better robustness against the sound speed errors. Therefore, the proposed beamformer offers a better performance than the DAS, MV and ESBMV beamformer, showing its potential in medical ultrasound imaging.     

基于任意阵的最小方差无失真响应聚焦波束形成的被动定位方法

将最小方差无失真响应(MVDR)聚焦波束形成近场被动定位方法应用到平面任意结构阵列上,可以很好地解决线列阵由于阵长较长,在实际使用中存在布放困难、部分阵元失效影响定位性能等问题。本文首先分析了平面任意结构阵列的近场接收信号模型,推导出阵列流型,然后将MVDR聚焦波束形成技术应用到该模型中,得到了基于任意阵的MVDR聚焦波束形成近场被动定位方法。仿真验证了该方法的可行性和有效性。     

信噪比后滤波与特征空间融合的最小方差超声成像算法

为了提高超声成像空间分辨率和对比度,提出了一种信噪比后滤波与特征空间融合的最小方差波束形成算法。首先,利用信号子空间划分将最小方差算法得到的权矢量投影到信号子空间中提高成像对比度,然后基于信号相干性设计滤波系数,并引入基于信噪比的噪声加权系数,最终得到融合信噪比后滤波与特征空间的最小方差算法。为验证本算法的有效性,使用FieldⅡ对点目标和吸声斑目标进行了仿真实验验证,并采用密歇根大学geabr_0实验数据进行成像。实验结果表明:所提算法在对比度和分辨率上均有所提高,明显优于传统延时叠加算法,最小方差算法和ESBMV_wiener算法,且对噪声具有较强鲁棒性。     

In vitro estimation of mean sound speed based on minimum average phase variance in medical ultrasound imaging

Effective receive beamforming in medical ultrasound imaging is important for enhancing spatial and contrast resolution. In current ultrasound receive beamforming, a constant sound speed (e.g., 1540 m/s) is assumed. However, the variations of sound speed in soft tissues could introduce phase distortions, leading to degradation in spatial and contrast resolution. This degradation becomes even more severe in imaging fatty tissues (e.g., breast) and with obese patients. In this paper, a mean sound speed estimation method where phase variance of radio-frequency channel data in the region of interest is evaluated is presented for improving spatial and contrast resolution. The proposed estimation method was validated by the Field II simulation and the tissue mimicking phantom experiments. In the simulation, the sound speed of the medium was set to 1450 m/s and the proposed method was capable of capturing this value correctly. From the phantom experiments, the −18-dB lateral resolution of the point target at 50 mm obtained with the estimated mean sound speed was improved by a factor of 1.3, i.e., from 3.9 mm to 2.9 mm. The proposed estimation method also provides an improvement of 0.4 in the contrast-to-noise ratio, i.e., from 2.4 to 2.8. These results indicate that the proposed mean sound speed estimation method could enhance the spatial and contrast resolution in the medical ultrasound imaging systems.     

Generalized sidelobe canceler beamforming applied to medical ultrasound imaging

A generalized sidelobe canceler (GSC) approach is proposed for medical ultrasound imaging. The approach uses a set of adaptive weights instead of traditional non-adaptive weights, thus suppressing the interference and noise signal of echo data. In order to verify the validity of the proposed approach, Field II is applied to obtain the echo data of synthetic aperture (SA) for 13 scattering points and circular cysts. The performance of GSC is compared with SA using boxcar weights and Hamming weights, and is quantified by the full width at half maximum (FWHM) and peak signal-to-noise ratio (PSNR). Imaging of scattering point utilizing SA, SA (hamming), GSC provides FWHMs of 1.13411, 1.68910, 0.36195 mm and PSNRs of 60.65, 57.51, 66.72 dB, respectively. The simulation results of circular cyst also show that GSC can perform better lateral resolution than non-adaptive beamformers. Finally, an experiment is conducted on the basis of actual echo data of an ultrasound system, the imaging result after SA, SA (hamming), GSC provides PWHMs of 2.55778, 3.66776, 1.01346 mm at z = 75.6 mm, and 2.65430, 3.76428, 1.27889 mm at z = 77.3 mm, respectively.     

平面波超声成像中的波束合成方法研究进展

平面波成像通过单次全孔径发射-接收即可获取整幅图像,将成像帧频显著地提升至1000帧/秒以上.然而,平面波成像过程中发射的非聚焦波束将导致回波信号信噪比降低,进而使图像的分辨率和对比度变差.通过多角度相干复合成像技术可以改善平面波成像的图像质量,但是会以牺牲帧频为代价.因此研究人员们开始将新型波束合成技术引入平面波成像...     

Adaptive field-of-view imaging for efficient receive beamforming in medical ultrasound imaging systems

Quadrature demodulation-based phase rotation beamforming (QD-PRBF) is commonly used to support dynamic receive focusing in medical ultrasound systems. However, it is computationally demanding since it requires two demodulation filters for each receive channel. To reduce the computational requirements of QD-PRBF, we have previously developed two-stage demodulation (TSD), which reduces the number of lowpass filters by performing demodulation filtering on summation signals. However, it suffers from image quality degradation due to aliasing at lower beamforming frequencies. To improve the performance of TSD-PRBF with reduced number of beamforming points, we propose a new adaptive field-of-view (AFOV) imaging method. In AFOV imaging, the beamforming frequency is adjusted depending on displayed FOV size and the center frequency of received signals. To study its impact on image quality, simulation was conducted using Field II, phantom data were acquired from a commercial ultrasound machine, and the image quality was quantified using spatial (i.e., axial and lateral) and contrast resolution. The developed beamformer (i.e., TSD-AFOV-PRBF) with 1024 beamforming points provided comparable image resolution to QD-PRBF for typical FOV sizes (e.g., 4.6% and 1.3% degradation in contrast resolution for 160 mm and 112 mm, respectively for a 3.5 MHz transducer). Furthermore, it reduced the number of operations by 86.8% compared to QD-PRBF. These results indicate that the developed TSD-AFOV-PRBF can lower the computational requirement for receive beamforming without significant image quality degradation.     

New ultrasound imaging techniques with phase coherence processing

This work addresses three key subjects to the image quality with phased arrays: timing accuracy, beamforming strategy and post-processing for increased resolution and suppression of grating and side lobes.Timing accuracy is achieved by defining a modular and scalable architecture which guarantees low timing errors, whatever is the system size. The proposed beamforming methodology follows the progressive focusing correction technique, which keeps low focusing errors, provides a high information density and has a simple implementation for real-time imaging in modular architectures. Then, phase coherence imaging is defined to suppress grating and sidelobe indications, simultaneously increasing the lateral resolution.     

基于被动波束成像的高强度聚焦超声焦点定位*

针对高强度聚焦超声治疗中实际焦点偏离引导治疗的几何焦点这一问题,本文基于被动波束成像原理,将治疗探头与影像探头联合,探讨一种新的焦点定位策略。首先,基于k-Wave声学软件创建了具有多层组织结构的超声传播模型,通过理论仿真探究了不同脂肪、肌肉厚度条件下被动波束成像方法定位焦点的准确性;除此之外,还通过仿体在实验环境下对该方法的有效性进行了初步验证。结果表明,在不同脂肪厚度的仿真模型中,被动波束合成所定位的焦点和几何焦点在轴向距离上分别与实际焦点平均相差（0.54±0.15）mm和（4.76±0.95）mm。在不同肌肉厚度的仿真模型中,由合成算法定位的焦点和几何焦点在轴向距离上分别与实际焦点平均相差（0.51±0.26） mm、（4.95±0.47） mm。仿体实验也验证了被动波束成像定位的焦点比几何焦点更接近实际损伤位置。本文从理论仿真和实验两个角度验证了被动波束成像方法在高强度聚焦超声焦点定位中的优越性,可为该方法进一步走向临床应用提供支撑,对提升高强度聚焦超声手术治疗的安全性有推动作用。     

医学超声影像产业现状和发展

本文结合超声影像产业的现状，分析了国内产业基础、市场需求，新技术发展趋势及提出有关新产品、新技术开发和产业发展的建议。其中包括重视原创产品的研究；开发有自主知识产权的新产品；调整产品结构，适应医疗体制改革和医疗保险制度；产业的发展应遏制医疗费用的膨胀，促进我国医学模式的转变等。     

Comparing tongue shapes from ultrasound imaging using smoothing spline analysis of variance

Ultrasound imaging of the tongue is increasingly common in speech production research. However, there has been little standardization regarding the quantification and statistical analysis of ultrasound data. In linguistic studies, researchers may want to determine whether the tongue shape for an articulation under two different conditions (e.g., consonants in word-final versus word-medial position) is the same or different. This paper demonstrates how the smoothing spline ANOVA (SS ANOVA) can be applied to the comparison of tongue curves [Gu, Smoothing Spline ANOVA Models (Springer, New York, 2002)]. The SS ANOVA is a technique for determining whether or not there are significant differences between the smoothing splines that are the best fits for two data sets being compared. If the interaction term of the SS ANOVA model is statistically significant, then the groups have different shapes. Since the interaction may be significant even if only a small section of the curves are different (i.e., the tongue root is the same, but the tip of one group is raised), Bayesian confidence intervals are used to determine which sections of the curves are statistically different. SS ANOVAs are illustrated with some data comparing obstruents produced in word-final and word-medial coda position.