超声成像方法综述

目的 讨论超声成像的主要方法、规律和历史。方法 阐述了各种成像方法的历史、原理、特点、用途和发展状况。结果与结论 指出了超声成像的发展方向。     

医学超声成像的模拟研究

简述医学超声成像模拟研究的主要方法,探讨其研究意义和应用前景。将现有的超声成像模拟算法分为基于线性假设和基于非线性假设的两大类,介绍每类中代表方法的基本原理,并讨论两类方法的差异,其中基于数值方法求解超声波动方程的模拟算法更符合实际超声成像过程,为更好地理解超声图像中的各种现象、寻求组织特性和图像特征间的匹配关系提供了有效的研究工具。     

多阵元合成孔径聚焦超声成像研究

对多阵元合成孔径聚焦超声成像进行了全面的研究.根据声场的辐射理论推导了多阵元孔径的声压分布,并分别对单阵元、多阵元聚焦和多阵元非聚焦的声场和缺陷响应进行了仿真,发现多阵元非聚焦的声场更适合于合成孔径聚焦超声成像,并根据这个原则进行了成像实验,结果表明在远场情况下,多阵元合成孔径聚焦超声成像可以获得比单阵元合成孔径聚焦成像更高的成像质量和分辨力.     

基于压缩感知的复合材料板Lamb波场重构及损伤成像

为了解决全波场数据获取耗时的问题,引入压缩感知算法对波场进行稀疏表示.然而受限于导波在复合材料中复杂的传播特性,压缩感知在复合材料板中的应用成为难题.为此通过考虑导波在复合材料板中传播的各向异性波数特性,构建计算频带内不同角度下的波数库用于波场重构.同时在损伤分析阶段,提出了一种无需基准信号的复合材料损伤散射波场分离技...     

声成像技术及其在医学超声中的应用

由于声成像技术具有对材料内部力学特性进行成像检测的特点,它已成为医学超声中的重要研究领域。为了提高声成像的空间分辨力,近年来,又发展了光声成像,扫描电子声显微术和扫描探针声显微术等新的近场成像技术。结合同济大学声学研究所部分研究结果,对这些近场成像技术在医学超声中的应用作了简单介绍。     

压缩感知理论及其应用

信号采样是模拟的物理世界通向数字的信息世界的必备手段。多年来,指导信号采样的理论基础一直是著名的Nyquist采样定理,但其在采样过程中产生的大量数据浪费了存储空间,而在压缩过程又会丢弃大量采样资源。压缩感知（Compressed Sensing）提出一种新的采样理论,它能够以远低于Nyquist要求的采样速率采样信号。本文详述了压缩感知的基本理论,着重介绍了信号稀疏变换、观测矩阵设计和重构算法三个方面的最新进展,并介绍了压缩感知目前有待解决的几个关键问题,最后举例说明压缩感知理论在各领域的应用。     

基于压缩感知的水声数据压缩与重构技术

在水声信号处理中,数据量大造成的数据处理压力不容忽视。为了有效地提取水声数据中的有用信息,同时缓解数据量大带给水声数据传输的压力,研究压缩感知(Compressed Sensing,CS)的基本原理及其关键技术,综述了CS理论框架并着重介绍了稀疏变换、观测矩阵设计和重构算法三个方面。通过仿真实验表明了压缩感知技术能够有效地用于模拟数据的压缩与重构。重点对水声舰船噪声信号进行了基于CS的压缩与重构仿真实验,验证了压缩感知技术运用于水声数据处理的有效性,从而达到提高水声数据传输速率的目的。     

合成孔径聚焦超声成像在混凝土探伤中的应用研究

针对超声波在混凝土检测中分辨率低、成像质量差等问题,提出了采用合成孔径聚焦技术对检测声波信号进行成像处理的方法。通过超声波仿真软件WAVE 2000建立混凝土缺陷模型,结合混凝土超声探测的方式和特点,采用MATLAB软件编写相适应的合成孔径算法及GUI界面。结果表明,在混凝土超声探伤中,此方法确实有效地提高了成像质量。     

广义轮换测量矩阵及其在水下回波信号压缩感知中的应用

根据压缩感知中测量矩阵的性质及要求，在轮换确定性测量矩阵的基础上，通过调整测量矩阵每一列元素的系数，增强列与列之间的相关性，得到广义轮换测量矩阵，并将其应用于水下回波信号的压缩感知观测中。通过无噪声下不同测量矩阵匹配度和相对误差随压缩比的变化，以及4、0、-3 dB三种信输入噪比下不同测量矩阵的输出信噪比、匹配度随压缩比的变化，分别对水下回波信号进行处理，比较其性能。仿真结果表明，相比部分哈达玛等确定性测量矩阵和以高斯为代表的随机测量矩阵，广义轮换测量矩阵在输出信噪比、匹配度、相对误差等方面有很大提高。同时广义轮换矩阵为确定性测量矩阵，便于工程实现。     

超声反射CT在水下物体成像中的应用

本文重点介绍了沿平面扫描成像，提出了修正扫描起始点位置以及换能器孔径影响的一种方法，得到较好的实验结果。     

Guided compressive sensing single-pixel imaging technique based on hierarchical model

Single-pixel imaging has emerged a decade ago as an imaging technique that exploits the theory of compressive sensing. In this research, the problem of optimizing the measurement matrix in compressive sensing framework was addressed. Thus far, random measurement matrices are widely used because they provide small coherence. However, recent reports claim that measurement matrix can be optimized, thereby improving its performance. Based on such proposition, this study proposed an alternative approach of optimizing the measurement matrix in a hierarchical model. In particular, this study constructed the hierarchical model based on an increasing resolution grade by exploiting the guided information and the adaptive step size method. An image with a demanded resolution was then obtained using the l1-norm method. Subsequently, the performance of the introduced method was verified and compared with those of existing approaches via several experiments. Results of the tests indicated that the reconstruction quality of optimizing the measurement matrix was improved when the proposed method was used.     

A new approach to radionuclide imaging using compressed sensing

Abstract

Single-photon emission computerised tomography (SPECT) and positron emission tomography (PET) are essential medical imaging tools, with inherent drawback of slow data acquisition process. We present a novel compressed sensing-based reconstruction of these images from significantly fewer measurements than traditionally required, thus demonstrating potential of reduction in scan time and radiopharmaceutical doze with benefits for patients and health care economics. Our work effectively shows that high fidelity two-dimensional (2D) SPECT/PET image is reconstructed using compressive sensing with considerably reduced numbers of samples in acquisition stage. The reconstruction of tomographic images is realised by compressed sensing the 2D Fourier projections of k-space data. These 2D projections being sparse in transform domain need fewer samples in k-space and are reconstructed without loss of fidelity. These undersampled Fourier projections can then be backprojected by employing the iterative reconstruction approach for a complete three-dimensional (3D) volume. Compressed sensing of a phantom image and PET bone scintigraphy with radial Fourier samples are performed. The reconstructions of these images are compared to conventionally sampled images using image quality measures like mean square error, peak signal-to-noise ratio and structure similarity (SSIM) index, showing high-quality image reconstruction.     

Optical image encryption based on compressive sensing and chaos in the fractional Fourier domain

We propose a novel image encryption algorithm based on compressive sensing (CS) and chaos in the fractional Fourier domain. The original image is dimensionality reduction measured using CS. The measured values are then encrypted using chaotic-based double-random-phase encoding technique in the fractional Fourier transform domain. The measurement matrix and the random-phase masks used in the encryption process are formed from pseudo-random sequences generated by the chaotic map. In this proposed algorithm, the final result is compressed and encrypted. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys for distribution simultaneously. Numerical experiments verify the validity and security of the proposed algorithm.     

Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery

The theory and application of compressive sensing (CS) have received a lot of interest in recent years. The basic idea in CS is to use a specially-designed sensor to sample signals that are sparse in some basis (e.g. wavelet basis) directly in a compressed form, and then to reconstruct (decompress) these signals accurately using some inversion algorithm after transmission to a central processing unit. However, many signals in reality are only approximately sparse, where only a relatively small number of the signal coefficients in some basis are significant and the remaining basis coefficients are relatively small but they are not all zero. In this case, perfect reconstruction from compressed measurements is not expected. In this paper, a Bayesian CS algorithm is proposed for the first time to reconstruct approximately sparse signals. A robust treatment of the uncertain parameters is explored, including integration over the prediction-error precision parameter to remove it as a “nuisance” parameter, and introduction of a successive relaxation procedure for the required optimization of the basis coefficient hyper-parameters. The performance of the algorithm is investigated using compressed data from synthetic signals and real signals from structural health monitoring systems installed on a space-frame structure and on a cable-stayed bridge. Compared with other state-of-the-art CS methods, including our previously-published Bayesian method, the new CS algorithm shows superior performance in reconstruction robustness and posterior uncertainty quantification, for approximately sparse signals. Furthermore, our method can be utilized for recovery of lost data during wireless transmission, even if the level of sparseness in the signal is low.     

A compressive sensing algorithm using truncated SVD for three-dimensional laser imaging of space-continuous targets

For traditional array 3-D laser radars, the resolution of the intensity image and range profile is limited by the number and accuracy of sensors. Moreover, for a space-continuous target, peak detection in the pulsed time of flight is no longer suitable for super-resolution reconstruction algorithms. Hence, a compressive sensing algorithm for 3-D laser imaging is proposed. A range observation matrix composed of time interval basis vectors is constructed to acquire the range information regarding a target. However, the range observation matrix is generally ill-posed owing to the spatial continuity of the target. To address this shortage, truncated singular value decomposition is utilized to extract the peak values of echo pulses for image reconstruction. Simulation results demonstrate the effectiveness and performance of the proposed algorithm.     

编码发射技术在医学高频超声成像中的研究进展

高频超声成像以其高分辨和实时性好等优势成为近年来医学超声成像领域研究的热点。但由于超声的衰减随频率增加呈指数级升高,导致高频超声信号幅值小,图像信噪比低。为了增强高频超声成像的信噪比,近年来编码发射（又称编码激励）技术得到了越来越多的研究和应用。文章对近几年国内外编码发射技术在医学高频超声成像方面的应用和研究进展进行了综述,重点分析了Chirp码、巴克（Barker）码和格雷（Golay）码3种主要编码发射技术在高频超声成像方面应用的优缺点,并综合对比了各种编码发射技术在高频成像方面的应用前景。     

红外热成像聚焦超声声场测量方法综述

基于红外热成像技术对聚焦超声声场进行快速、定量测量的方法具有扫描速度快、空间分辨率高、适用频率范围广等优点。但是由于红外线的穿透能力限制,在红外摄像仪和超声吸收体之间需要一个空气层,因此使得超声吸收体内部声场较为复杂,目前已有三种模型对此进行描述,且都得到了相应的实验验证。本文通过分析基于三种模型的超声吸收体内部声场分布和声强估计方法,对其进行了较为详细的描述。为进一步研究该项技术提供了理论参考依据。     

Synthetic‐aperture technique for high‐resolution composite imaging of the inside walls of tubular specimens

High‐resolution survey of the inside walls of tubular specimens is a unique application of synthetic‐aperture composite imaging. This article describes the data acquisition process, 3D motion estimation and compensation, image registration, and superposition for the formation of high‐resolution composite images from conventional video sequences. Experiment results from the survey of an oil well are used to demonstrate the capability of the technique. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 167–169, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20020     

Broadband holography applied to eddy current imaging by using signals with multiplied phases

In this paper an eddy current imaging method for nondestructive testing purposes is presented which utilizes the concept of broadband holography. An eddy current coil which is used simultaneously as an antenna for eddy current generation and as a probe for detection of response of interaction between eddy currents and flaws, respectively, is moved along a synthetic aperture during the imaging procedure generating synthetic eddy current pulses by scanning a certain frequency range. In terms of wave propagation phenomena the penetration depth (range) of eddy currents in conducting media is small compared to the equivalent wavelength of this type of fields. Therefore, adequate resolution can only be obtained in the reconstructed cross-sectional images by phase multiplication of received multifrequency signals, which is equivalent to a fictitious reduction of wavelengths. Experimental results verify the imaging capability of this method with improved resolution compared to conventional eddy current testing methods.