正电子发射型计算机断层成像技术

 正电子发射型计算机断层成像(positronemissioncomputedtomograph简称PET),其原理是将含有发射正电子的放射性核素,如18F、11C、15O、13N等显像剂注入或吸入人体,通过探测正电子放射性核素衰变时产生的正电子与组织内电子湮灭产生两个能量相等、方向相反的γ光子,由计算机重建图像,显示人体代谢和生化等改变,被誉为活体的分了断层图像。因此PET能将人的思维、行为和脑化学联系起来,探讨、解释和定位人脑的功能活动,对于许多精神、情感、功能及运动障碍等功能性疾病,PET具有理论意义和实用价值。     

中子半影成像的计算机断层重建技术

实际用于中子半影成像重建的方法都是线性方法,而计算机断层重建是一种能够用于中子半影成像重建的新方法.介绍了中子半影成像的断层重建原理,将半影成像的诊断图像沿径向等角度提取、微分后就可以使用断层重建方法重建.给出了使用卷积反投影方法重建不同噪声诊断图像的结果,并分析了断层重建的优缺点.重建结果表明,断层重建方法能够重建出...     

单光子发射型计算机断层成像

 单光子发射型计算机断层成像(SPECT),它与X-CT有某些相似之处,所不同的是:X-CT的X射线源位于体外,X射线透过组织时,根据不同组织对X射线的衰减值的不同,重建某断层的CT数矩阵,并用灰度来显示断层图像.     

正电子发射成像回旋加速器屏蔽材料防护性能

计算了11 MeV 正电子发射成像回旋加速器18O(p, n)18F反应的双微分截面数据,并采用蒙特卡罗模拟计算方法,分析了单一材料及复合结构对特定辐射源的防护性能。采用单一材料,质量分数为73.8%的重混凝土的防护效果最佳,经过90 cm厚屏蔽层的衰减,距离辐射源正前方1 m处的总剂量降低约5个量级;复合结构采用多层结构,材料选用铁及硼酸溶液,同样厚度即可达到与重混凝土相同的防护效果,密度可降低至少20%。     

应用蒙特卡罗模拟进行正电子发射断层成像仪散射特性分析

通过使用基于Geant4的蒙特卡罗模拟代码GATE对全身用全3D正电子发射断层成像仪和含有挡板的2D/3D小动物用正电子发射断层成像仪进行建模,系统地分析了3D采集条件下正电子发射断层成像仪的散射分数、散射分布、多次散射、视野外散射四个主要方面和2D采集条件下挡板对散射分数和散射分布的影响.针对全3D散射校正的难点: 多次散射和视野外散射,设计了附加实验,拟合得到了多次散射光子的百分比随体模横截面积变化的关系和不同环的位置受到视野外散射光子的影响;针对2D散射校正,对挡板引入的散射光子进行分离,单独分析, 关键词： 正电子发射断层成像仪（PET） 蒙特卡罗模拟 散射特性 散射校正     

并行MRI图像重建算法比较及软件实现

首先介绍了不加速的并行MRI图像重建方法,然后对加速的并行MRI的4种图像重建算法进行了比较,得出结论：加速因子相同时,重建质量上,GRAPPA和SENSE的重建质量最好,SMASH的重建质量次之, PILS算法对线圈位置要求极高,重建质量最差;重建速度上,SMASH的重建速度最快,其次是SENSE和PILS,GRAPPA的重建速度最慢. 当加速因子变大时,所有算法重建质量都变差. 最后介绍了算法实现软件,该软件可以读入原始数据,显示数据采集轨迹,计算线圈灵敏度,选择图像重建方法,分析和比较重建图像质量. 该软件为我国在MRI成像领域提供了一个学习和进一步研究图像重建算法的有力工具.     

基于反向传播神经网络的阻抗断层图像重建新方法

电阻抗成像是通过对物体表面电压、电流的测量来重建物体内部阻抗分布或变化图像的一种新颖计算机断层成像技术。阻抗断层图像重建是一种病态的、非线性的逆问题。提出了一种全新的阻抗断层图像重建方法，它利用反向传播神经网络来表征物体内部阻抗变化位置与物体表面电压变化大小的非线性映射关系，从而可以根据对物体表面测量电压的变化先准确定位阻抗变化区域，再用线性近似方法重建阻抗变化图像，这种方法不仅具有一定的抗噪能力，而且成像精度和空间分辨率都大大好于逆投影方法。     

稀疏张量约束的低剂量CT图像重建

提出了一种稀疏张量约束重建算法,该方法利用非局部相似的先验信息,将CT图像分割成一系列图像块组;采用张量的多维低秩分解方法,将这一先验信息引入低剂量CT重建中,构造目标函数;通过重建图像更新和图像块组张量稀疏编码两个步骤,交替迭代求解目标函数。基于仿真数据和临床数据的实验结果验证了该算法的有效性,实验结果表明:与经典重建算法相比,所提算法在抑制噪声的同时,能更好地保持重建图像的细节,获得更高质量的图像。     

激光共焦扫描显微镜三维数据场截面图像重建

提出利用插值技术实现离散三维数据场重采样的算法，并利用计算机图形学技术显示重建图像。     

Evaluation of imaging protocol for ECT based on CS image reconstruction algorithm

Single-photon emission computerized tomography and positron emission tomography are essential med- ical imaging tools, for which the sampling angle number and scan time should be carefully chosen to give a good compromise between image quality and radiopharmaceutical dose. In this study, the image quality of different ac- quisition protocols was evaluated via varied angle number and count number per angle with Monte Carlo simulation data. It was shown that, when similar imaging counts were used, the factor of acquisition counts was more important than that of the sampling number in emission computerized tomography. To further reduce the activity requirement and the scan duration, an iterative image reconstruction algorithm for limited-view and low-dose tomography based on compressed sensing theory has been developed. The total variation regulation was added to the reconstruction process to improve the signal to noise Ratio and reduce artifacts caused by the limited angle sampling. Maximization of the maximum likelihood of the estimated image and the measured data and minimization of the total variation of the image are alternatively implemented. By using this advanced algorithm, the reconstruction process is able to achieve image quality matching or exceed that of normal scans with only half of the injection radiopharmaceutical dose.     

PITRE: software for phase‐sensitive X‐ray image processing and tomography reconstruction

Synchrotron‐radiation computed tomography has been applied in many research fields. Here, PITRE (Phase‐sensitive X‐ray Image processing and Tomography REconstruction) and PITRE_BM (PITRE Batch Manager) are presented. PITRE supports phase retrieval for propagation‐based phase‐contrast imaging/tomography (PPCI/PPCT), extracts apparent absorption, refractive and scattering information of diffraction enhanced imaging (DEI), and allows parallel‐beam tomography reconstruction for conventional absorption CT data and for PPCT phase retrieved and DEI‐CT extracted information. PITRE_BM is a batch processing manager for PITRE: it executes a series of tasks, created viaPITRE, without manual intervention. Both PITRE and PITRE_BM are coded in Interactive Data Language (IDL), and have a user‐friendly graphical user interface. They are freeware and can run on Microsoft Windows systems via IDL Virtual Machine, which can be downloaded for free and does not require a license. The data‐processing principle and some examples of application will be presented.     

基于最小区域的锥束ART算法快速图像重建

针对锥束ART算法重建速度慢的问题,考虑到工业CT重建目标尺寸差异较大的特点,提出了一种基于最小重建区域的快速三维图像重建方法。由不同视角下的锥束投影构建最小区域包络,对最小区域包络进行膨胀处理,以消除重建目标边界附近所出现的伪影,在此基础上,给出了射线穿过最小重建区域的体素索引及权因子的计算。该方法能够根据重建目标的尺寸自适应地确定最小重建区域,从而最大限度地减少了计算量。实验结果表明,该方法不仅大幅度提高了锥束ART算法的重建速度,而且有效地提高了重建质量。     

层析成像图像重建算法综述

介绍了层析成像技术的图像重建算法,并从正向问题数学模型的简化和反向问题数学模型的映射结构的角度比较了各种算法的特点和优劣。研究表明:用本质是线性算法的各种变换方法重建图像存在严重失真,而卷积滤波的引入可以使变换方法的重建效果有所改善;基于导数搜索的迭代算法对初始值依赖性强、收敛速度慢并且容易陷入局部最优解;基于Fourier变换的方法具有本质的局限性;小波变换则可以同时刻画图像时域和频域的细节特征;有限元法通过重建对象像素的智能划分可以简化正问题的复杂性;而具有物理背景的蒙特卡罗法、模拟退火法、遗传算法、粒子滤波法及神经网络法更适合于复杂且非线性的图像重建;智能化、仿生化、并行化以及各种算法的融合是层析成像图像重建算法的发展趋势。     

基于稀疏优化的计算机断层成像图像不完全角度重建综述

计算机断层成像(computed tomography,CT)技术在医学和工业无损检测中都具有非常广泛的应用,CT重建算法是其中的核心,而不完全角度重建问题则是实际应用中重建算法研究领域的一个热点和难点问题.近年来,随着稀疏优化理论与算法的飞速发展,基于稀疏优化的重建算法已经在不完全角度重建问题中得到了较广泛的应用,且表现出了良好的精度与速度性能.本文首先对稀疏优化的基本理论结论与常用算法进行了介绍;而后对稀疏优化理论在CT图像不完全角度重建中的应用进行归纳,分类介绍了其主要研究成果及稀疏优化所发挥的作用;最后对基于稀疏优化的不完全角度重建研究进行了展望.     

An algorithm for computed tomography image reconstruction from limited-view projections

<正>With the development of the compressive sensing theory,the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology.This paper develops an iterative algorithm for image reconstruction,which can fit most cases.This method gives an image reconstruction flow with the difference image vector,which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough.Then the l_2-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles.The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data.Moreover,all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed.     

An image reconstruction algorithm based on Bayesian theorem for electrical resistance tomography

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for two-phase/multiphase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The iterative image reconstruction algorithm based on Bayesian theorem has been derived to solve the ERT inverse problem. It is taken into account, which is different to the existing ERT image reconstruction algorithms, including the prior probability of the permittivity distribution and the noise information in the measurement data. Both simulation and experimental data were carried out for typical two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the Bayesian iteration method and conventional methods. Results obtained indicate that the Bayesian iteration method improves the reconstructed image quality with the traditional linear back projection (LBP) and reweighted least squares (RLS). Therefore, the Bayesian iteration method is suitable for identification of online two-phase flow regimes.     

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography(CT) reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.     

Deep learning for image reconstruction in thermoacoustic tomography

Microwave-induced thermoacoustic tomography(TAT)is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging.While an image reconstruction algorithm is critical for the TAT,current reconstruction methods often creates significant artifacts and are computationally costly.In this work,we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image.We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy.For the scenarios where realistic training data are scarce or unavailable,we use the finite element method(FEM)to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method.The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness.Compared with other state-of-the-art reconstruction methods,the TAT-Net method can reduce the root mean square error to 0.0143,and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64,respectively.The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.     

基于模糊隶属度的图像空间距离修正插值算法

为解决传统图像插值算法存在的边缘模糊和边缘锯齿,提出了一种基于像素点模糊隶属度的图像自适应插值方法.该方法首先根据图像的梯度与相角特性,确定像素点的模糊隶属度,再根据图像的局部不对称性在一维方向上修正插值点空间距离,并将一维修正结果转化到二维图像空间,最终将修正后的空间距离应用到传统双线性插值和双立方插值中.实验结果表明,该算法改善了图像的信噪比,有效抑制了边缘锯齿和边缘模糊的发生.