基于自适应投影增强的压缩感知CT图像重建

针对投影数据受噪声影响严重、被检测工件体积大获取投影数据不完全等导致重建质量差的问题,提出一种自适应反锐化掩模算法对某发动机模拟件投影数据均匀抽取60个角度投影进行预处理,根据其边缘信息设定阈值进行自适应去噪并对高频信息乘以自适应增益增强,采用TV最小化约束的代数重建(ART)算法重建.将改进算法与经典ART、经典反锐化掩模技术处理投影以及投影未处理的POCS-TVM算法进行对比分析,实验结果表明,该方法能够平滑噪声,提高对比度,进而提升不完全投影重建图像质量.重建同样大小的图像,相对于全角度投影迭代一次时间100 s, 60个角度投影迭代一次18.7 s,时间缩短近6倍,大量节省重建时间.     

松驰参数对图像重建的影响

通过代数迭代算法在不同松驰参加数下重建图像的仿真研究,分析在相同迭代次数下松驰参数对重建图像收敛速度的影响。对含无噪声和有噪声的投影数据重建图像,合适的松驰参数能在较少迭代次数和较短时间内得到好的重建图像。     

基于行灰度投影相关的快速角度矢量估计算法

针对现存电子稳像算法估计图像旋转角度矢量的不足,提出了一种基于行灰度投影相关的快速图像旋转角度矢量估计算法。通过在当前帧图像中心位置选取灰度投影区域和参考帧图像相应区域作行灰度投影相关计算,估算出当前帧图像相对于参考帧图像的角度运动矢量。论述了灰度投影区域选取原则;分析了行、列灰度投影相关曲线的差异,得出行灰度投影相关曲线具有单峰性的结论;阐述了快速搜索算法的原理及其在本算法中的应用。实验表明,算法实现了对旋转图像序列角度运动矢量的快速估计（在PⅣ2.67GHZ,内存512M的PC机上,估算速度为37.7ms）,可对任意角度的角度矢量进行高精度的估计。     

ROI-ART重建算法的仿真研究

以ART重建图像的算法为研究对象,介绍不完全投影数据相适应的算法。重点介绍ROI引入到ART算法的计算机仿真研究。     

基于序列图像中二次曲线对应的纯旋转运动参数估计算法

采用特征对应的视觉运动分析方法，建立了基于二次曲线的刚体纯旋转运动方程，提出了求解刚体纯旋转运动参数的算法。在刚体纯旋转运动分析中，最少利用前后两幅图像中7对匹配二次曲线，就可用线性方程组求解刚体旋转运动参数，线性算法的提出使旋转运动参数的求解精度得到很大提高。最后利用合成数据和真实图像对纯旋转运动估计算法的精度和有效性进行了验证，表明该算法能准确、可靠地估计旋转运动参数。     

基于CT系统的两相流包络检测系统研究

计算机断层扫描技术(CT)是有效的非接触测量手段。论文设计了一套用于检测管道中两相流介质占比包络的CT图像重建系统。管道中流体即测量目标的状态随时间不断变化,传统的旋转CT系统由于需要等待照影过程实时性不足。本系统使用五边形结构的探测器组,相比传统的旋转CT系统优点是位五个方向的探测器立即获得照影数据,在探测器数据的基础上实时生成CT重建图像,不必等待探测器完成照影。重建图像的质量由照影数据量决定,不同于旋转CT系统长时间照影获得丰富的数据能得到高质量图像,本系统照影时间短,实时性高,数据不足将导致图像失真。为消除图像失真对测量结果的不利影响,CT重建系统在图像后期处理算法中使用数据反演技术和系统校准两种手段修正图像失真。重建系统的目的在于确定密封管道中两相流流体包络,根据管道尺寸等先验信息确定图像边界,丢弃多余的失真图像数据保留需要的流体边界,完成对管道中两相流流体边界包络检测。     

基于多向投影新方法的捆钞印章个数统计

提出了一种快速自动统计印章个数的多向投影新方法。该方法基于图像处理理论,将采集到的捆钞图像从RGB空间转为LUV空间,然后通过聚类分析算法,得到捆钞的二值化图像,再利用多向投影的思路,依次在垂直和水平方向投影变换,完成印章个数的统计。理论分析及实验结果表明,该方法可以有效去除背景噪声的影响,不依赖于印章印迹的好坏,提高了印章个数自动统计的效率和准确率。     

基于梯度投影稀疏重建算法的电容层析成像图像重建

为解决两相流中存在中心物体、物体比较小或存在多个物体且相距较近时电容层析成像(ECT)重建图像精度较差的问题,基于稀疏分布的流型其介电常数分布满足稀疏性的先验条件,采用梯度投影稀疏重建(GPSR-BB)算法进行ECT图像重建。仿真及实验测试结果表明:GPSR-BB算法对于流体中小目标以及复杂流型的图像重建质量较好,重建图像的形状保真度高。     

基于固定扫描点的建筑物三维测量方法

提出一种基于固定激光扫描点的三角投影法测量建筑物大目标三维尺寸的方法。利用线结 构光旋转扫描形面和对投影图像进行处理分析,实现三维重建和测量。导出了三维几何尺寸的计算公式,进行了仿真实验,获得满意的数据。在进行建筑物等大目标三维测量时,不需激光器和传感器平行移动,减少误差来源,提高精度,方便可行。     

分解算法的丢失数据射影重建

在射影重建问题中,广泛应用的分解算法尽管能够平等地看待所有的图像,但它们要求全部空间点在所有的图像上都可视.为了克服这一限制,该文提出了一种基于分解算法的射影重建方法.该方法迭代地估计空间点、投影矩阵、深度因子和丢失的数据.投影矩阵和空间点通过奇异值分解被求取.深度因子则根据每幅所有图像点构成的行向量生成的线性子空间和与射影重建空间点构成的行向量生成的线性子空间是同一线性子空间的特性被估计出来.模拟数据和真实图像的实验结果表明,该射影重建方法具有重投影误差小,收敛性好和实用性强的特点.     

Investigation of the Precision of an Algebraic Algorithm for the Reconstruction of Tomograms from Projections Given in a Limited Range of Angles

The solution of the problem of reconstructing an image of the cross-section of an object with a limited set of data is considered. A program developed on the basis of the ART3 algebraic algorithm for reconstruction of tomograms is described. Results from a computational experiment are presented; in the course of the experiment the precision of a two-dimensional tomographic reconstruction as a function of the range of angles and number of projections obtained for models of cells with and without a nucleus is studied. __________ Translated from Izmeritel'naya Tekhnika, No. 8, pp. 34–37, August, 2005.     

Performance evaluation of iterative tomography algorithms for incomplete projection data

Projection data obtained through optical techniques for tomographic measurements, such as interferometry for refractive-index-based measurements, are often incomplete. This is due to limitations in the optical system, data storage, and alignment and vignette issues. Algebraic iterative reconstruction techniques are usually favored for such incomplete projections. A number of iterative algorithms, based on additive and multiplicative corrections, are used with a known simulated phantom and noise source to assess the reconstruction performance of incomplete data sets. In addition, we present reconstructions using experimental data obtained from a coherent gradient sensing interferometer for a steady temperature field in a fluid medium. We tested the algorithms using the simulated data set for incompleteness conditions similar to those found in the experimental data, and the best-performing algorithm is identified.     

油水两相流浓度电容层析成像的测量方法

以8电极油水两相流电容层析成像系统为研究对象,分析了油水两相流电容层析成像系统的测量原理,依据二重积分微元原理建立了油水两相流相浓度分布信息的数学模型,采用ART图像重建的方法完成油水两相流的浓度测量,并在重建算法中引入了阈值以提高图像重建质量和含率计算的准确性,测量误差在1%左右,较好地解决了其他测量方法速度慢,成本高,安全性差等缺陷,为油水两相流浓度的测量提供了一种新方法.     

超声衍射CT及其在不完全投影条件下的实验研究

超声衍射层析成像术是一种利用声波散射数据所携带的信息来反演物体内部结构的技术，具有广泛的应用前景。本文对这一领域的研究现状进行综述，慨述一些典型的超声衍射层析成像重构方法，同时探讨在残缺投影数据下的重构问题，对于不完全投影数据下的图像重构进行了实验研究。最后对实验结果进行讨论。     

Column-relaxed algebraic reconstruction algorithm for tomography with noisy data

A tomographic reconstruction algorithm similar to the well-known algebraic reconstruction technique (ART) is presented. Similar to ART, the approximate algebraic reconstruction technique (AART) technique consists of a sequence of displacements of the image vector based on the projection error. AART is a column-relaxation technique that is a series of vector displacements of the image vector parallel to its coordinate axes. AART is compared with ART, a standard conjugate-gradient technique, and a conjugate-gradient technique augmented by nonnegativity. The use of relaxation parameters to improve the performance of both ART and AART in the presence of noise is discussed, and the use of an iteration termination criterion based on random generalized cross validation is illustrated.     

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.     

A novel iterative optimizing quantization technique and its application to X-ray tomographic microscopy for three-dimensional reconstruction from a limited number of views

The iterative optimizing quantization technique (IOQT) is a novel method in reconstructing three-dimensional (3D) images from a limited number of 2D projections. IOQT can reduce the artifacts and image distortion due to a limited number of projections and limited range of viewing angles. Equivalently, by reducing the number of projections required for reconstruction, the use of IOQT can reduce the dose delivered to the specimen, simplify the complexity of an experimental setup, and consequently support the development of techniques to nondestructively image microstructures of materials. In this article, we will demonstrate the capability of IOQT to reconstruct an accurate 3D image of an object from a limited number of views, using a computer simulation and an actual 3D test pattern experiment with submicrometer features. In addition, we will introduce a promising application of IOQT to X-ray tomographic microscopy to study microbiological specimens by presenting the 3D reconstructions of the two different-conditioned human sperm cells from six projections. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 204–213, 1997     

Filtered backprojection algorithm can outperform iterative maximum likelihood expectation‐maximization algorithm

The iterative maximum‐likelihood expectation‐maximization (ML‐EM) algorithm is an excellent algorithm for image reconstruction and usually provides better images than the filtered backprojection (FBP) algorithm. However, a windowed FBP algorithm can outperform the ML‐EM in certain occasions, when the least‐squared difference from the true image, that is, the least‐squared error (LSE), is used as the comparison criterion. Computer simulations were carried out for the two algorithms. For a given data set the best reconstruction (compared to the true image) from each algorithm was first obtained, and the two reconstructions are compared. The stopping iteration number of the ML‐EM algorithm and the parameters of the windowed FBP algorithm were determined, so that they produced an image that was closest to the true image. However, to use the LSE criterion to compare algorithms, one must know the true image. How to select the optimal parameters when the true image is unknown is a practical open problem. For noisy Poisson projections, computer simulation results indicate that the ML‐EM images are better than the regular FBP images, and the windowed FBP algorithm images are better than the ML‐EM images. For the noiseless projections, the FBP algorithms outperform the ML‐EM algorithm. The computer simulations reveal that the windowed FBP algorithm can provide a reconstruction that is closer to the true image than the ML‐EM algorithm. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 114–120, 2012     

Reflective tomography reconstruction of satellite features-field results

We report on the first satellite feature reconstruction, to our knowledge, by use of range-resolved reflective tomography techniques from nonimaging laser radar data collected on an orbiting satellite. The reconstructed features were two retroreflectors mounted on a satellite. The data were collected with a coherent laser radar system located at the Maui Space Surveillance Site in Maui, Hawaii. We utilized a filtered backpropagation algorithm to reconstruct the images using intensity projections created from the raw data. Key components of the image reconstruction process included the calculation of tomographic angles, the alignment of the intensity projections to an appropriate center of rotation, and compensation for the lack of data for a full 360 degrees set of views.     

Similarities and differences in radiation therapy optimization and tomographic reconstruction

Modern photon radiotherapy optimization methods require the use of a number of nonuniform dose distributions incident on the tumor. From this point of view, radiotherapy optimization has strong similarities with the reconstruction problem in tomographic imaging. In general, the image reconstruction problem is simpler because in the absence of noise and with sufficiently many projections and exact solution always exists. However, it is in general impossible by external beam irradiation to produce an arbitrary desired dose distribution in the patient. This is primarily because the order of events from physical collection of projection data to reconstruction theory is reversed in therapy optimization, starting with the theory and ending with physical irradiation, where negative dose delivery is impossible. Despite this fundamental problem, many approximate image reconstruction methods work quite well for therapy optimization even though strict optimization requires radiobiological models and the finest external beam radiation tool available: the pencil beam.