应用变权全变分重建抑制CT金属伪影

提出一种基于变权全变分(RWTV)迭代重建的金属伪影校正算法以抑制CT系统重建图像中出现的金属伪影。该算法应用自定义的权值函数对全变分模型做加权惩罚,生成权值全变分模型;通过交替解权值全变分最小化过程和更新权值步骤实现变权全变分重建算法。应用该算法对数值模型和临床图像前向投影生成的投影数据分别进行了重建实验。数值模型实验结果表明:在60个采样角度下,用提出算法重建的图像具有最高的空间分辨率特性;且信噪比值较平滑插值金属伪影校正算法、全变分约束最优化算法的重建结果分别高出17.523 6和7.145 2dB。临床数据实验结果表明:该算法重建结果有效抑制了CT金属伪影,清晰重建出颅骨内的细节解剖结构,极大提高了重建图像的质量。     

电容层析成像图像重建中的迭代算法

电容层析成像技术中图像重建算法的准确与快速是其在工业实际中得以应用的关键。Landweber方法是一种简单、灵活的迭代算法，具有很好的正则化特性，它的投影变体能够提供非负的图像重建。Landweber算法实际应用的困难是其收敛速度太低。本文采用预处理方法来加快Landweber迭代方法的收敛速度，即通过较少的迭代次数，获得适当的重建结果。仿真实验表明，同经典的快速算法共轭梯度迭代方法相比，预处理Landweber迭代方法具有更好的重建图像质量。     

基于时间串流的 OSEM 图像重建算法研究

利用γ光子探测腔体内部动态流场需要快速的图像重建算法，传统处理方式是先采集所有事件、再进行OSEM等算法处理。本文提出了一种按时间流对响应事件进行子集划分的图像重建(T-OSEM)算法。在连续采样数据的同时，按时间段将采样到的数据划分为子采样数据集，对子集进行OSEM迭代实现图像重建。并将上一帧图像作为迭代输入，利用帧间图像相关性，以加快收敛速度。该算法中数据流的采样与上一帧图像的处理同时进行，并通过多线程并行运算加速图像重建过程。研究了最优子集事件数量及相对应采样时间的关系，以实现在尽可能短的采样时间下达到最优的重建效果。实验表明，当采样时间段达到1 s时，T-OSEM算法仍有很好的粒子跟踪效果，粒子轨迹图像结构相似比为0.92,表明T-OSEM算法对于动态图像重建是一个比较好的解决方案。     

流场超声层析成像中图像的迭代滤波反投影重建

为提高流场超声层析成像的图像重建质量,提出了一种迭代滤波反投影图像重建算法。该算法借鉴联合迭代重建算法的原理,将滤波反投影算法引入迭代重建过程。首先,利用滤波反投影算法,通过投影数据残差重建误差图像对流场图像进行修正,实现图像的迭代重建。然后,通过优化迭代步长,使每步迭代后投影数据残差均取得极小值以便加快收敛速度。最后,基于流场连续、紧支撑分布的特点,在迭代重建过程中引入投影数据的细分内插和流场图像的圆域修正。实验表明:相比于滤波反投影算法,迭代滤波反投影算法可使理论流场重建的图像误差平均减少26%,流量误差由1.77%减小至±0.25%以内;程序运行时间为0.63s,仅为联合迭代重建算法的0.89%。该算法可实现对直管段内和单弯管下游实际流场的可靠重建,满足流场高精度实时成像的要求。     

基于区域划分的红外超分辨率重建

提出了红外超分辨率重建系统以获取高分辨率红外数据。首先,根据红外图像获取过程建立了数学模型,讨论了降采样、模糊、运动以及高斯噪声对红外系统的影响;在非退化特征提取的基础上提出了基于特征的亚像素配准算法,其根据所得到的非退化特征应用归一化均方根误差来估计两帧之间的亚像素位移。然后,分析了传统全变分因子在高分辨重建时的不足并对其进行改进;利用区域划分将图像划分为平滑区域和细节区域,并根据区域的不同情况自适应全变分因子,从而使细节区域不至于过平滑。最后,利用MM(Majorization Minimixation)算法对合成的低分辨率红外图像和真实红外图像进行了超锐度重建。与同类相关算法的比较实验显示:所提算法亚像素配准最大误差为0.09pixel,重建后的红外图像质量优于其他同类算法。所提算法可以对低分辨红外图像序列进行有效重建,具有配准精度高、重建图像细节丰富等特点,可应用于各种红外成像系统。     

基于梯度直方图变换增强红外图像的细节

针对红外图像信噪比低、目标边缘和细节模糊等缺点,提出了一种基于梯度直方图变换增强红外图像细节的方法。通过分析红外图像的梯度直方图,构造出一个高斯函数来扩展梯度直方图,增大图像的梯度值。采用直方图规定化法,实现图像的梯度直方图规定化,得到变换的梯度场。在从变换的梯度场重建增强的图像时,引入全变分(TV)模型来抑制噪声。实验结果表明,提出算法的图像信息熵比灰度直方图均衡化和规定化算法显著提高,明显增强了红外图像的纹理细节,为目标检测、跟踪和识别提供了高质量的图像信息。提出的算法采用有限差分法迭代进行求解和Visiual C++编程,对大小为480pixel×480pixel的图像的处理时间约为40ms,基本达到了工程应用对图像处理的要求。     

基于差分吸收光谱技术的大气痕量气体重建算法

针对在一些不能获取大量投影数据或者投影分布不均匀的实际情况下,重建大气痕量气体的二维空间分布问题,采用代数迭代重建算法(ART)和同步迭代重建算法(SIRT)2种重建算法对其进行重建。通过数值模拟的方式对2种重建算法的重建效果进行了比较研究,并建立了多轴差分吸收光谱层析系统,进行了外场试验。通过计算机模拟和外场实验研究,从成像效果中可以看出,2种迭代算法均能在数据不完善的情况下,精确地重建出大气痕量气体的二维空间分布;在不同的模型及评价指标下,通过对2种重建算法的重建误差的比较,可以发现SIRT算法的重建误差要小于ART算法的重建误差,尤其在测量数据波动比较大、受干扰的情况下。     

光声成像反射伪影的机理分析和控制

在光声成像中,重建图像经常会受到图像伪影的影响而降低图像质量.为了解决图像伪影的问题,从超声波在水中的传播特性出发,分析了光声图像中伪影的产生机理.分析表明:伪影的产生是由超声信号在水面的反射延迟造成的.进而提出了通过延迟相减来控制反射伪影的方法,并给出了在实际应用中控制水面高度的依据.通过构建的光声成像平台进行实验验证,结果表明:提出的方法能有效地消除重建图像中的伪影,提高了重建图像的质量.     

用于电容层析成像的一步稳定图像重建算法

本文提出一种基于Landweber迭代的电容层析成像系统一步稳定图像重建算法。该算法通过构建压缩算子可以克服传统Landweber迭代方法存在的半收敛性,得到稳定的重建图像。同时该算法的矩阵算子可以预先计算,然后通过一步矩阵运算实现图像重构。实验结果表明,该算法可保证重建图像的实时性和鲁棒性。     

基于压缩感知的多角度观测光声成像方法

在光声成像过程中,由于各种限制因素,往往只能完成有限角度的信号采集.采集到的不完备的信号会在图像重构过程中产生伪迹现象,丢失细节信息.基于光声图像的稀疏特性,提出了基于压缩感知的光声成像算法.该方法通过2个单阵元超声探测器成角度采集光声信号,然后基于压缩感知重构算法进行光声图像重建并进行融合处理.仿真证明采用多个角度观测,选用合适的探头分布角度和测量矩阵可以有效弥补远场成像分辨率和减少测量次数,消除光声图像的伪迹现象,最终实现以较少的数据量和较简单的硬件设备实现高分辨率光声成像.     

A new nonlinear reconstruction method based on total variation regularization of neutron penumbral imaging

Neutron penumbral imaging is a significant diagnostic technique in laser-driven inertial confinement fusion experiment. It is very important to develop a new reconstruction method to improve the resolution of neutron penumbral imaging. A new nonlinear reconstruction method based on total variation (TV) regularization is proposed in this paper. A TV-norm is used as regularized term to construct a smoothing functional for penumbral image reconstruction in the new method, in this way, the problem of penumbral image reconstruction is transformed to the problem of a functional minimization. In addition, a fixed point iteration scheme is introduced to solve the problem of functional minimization. The numerical experimental results show that, compared to linear reconstruction method based on Wiener filter, the TV regularized nonlinear reconstruction method is beneficial to improve the quality of reconstructed image with better performance of noise smoothing and edge preserving. Meanwhile, it can also obtain the spatial resolution with 5 μm which is higher than the Wiener method.     

动态电容层析成像图像重建算法

提出了融合ECT测量信息和被测对象动态演化信息的新型图像重建模型;基于Tikhonov正则化方法,建立一个同时考虑了ECT测量信息、被测对象动态演化信息、时间与空间约束的新型图像重建目标泛涵,将图像重建问题转化为最优化问题;提出了集成分裂Bregman迭代法优势的新型算法求解该目标泛涵。数值仿真结果表明,所提出的图像重建算法其图像重建质量均优于OIOR算法、STR算法及PLI算法;同时由于所提出的图像重建算法同时考虑了测量数据和重建模型的不精确性,其抵抗测量噪声的能力得以提高。     

基于滤波反投影的脑磁感应迭代重建算法研究

颅脑磁感应断层成像技术(BMIT)是一种非接触、无创的新兴颅脑医学成像技术,图像重建算法是提高重建图像质量的关键。依据BMIT反投影算法和迭代算法,设计出一套基于滤波反投影的脑磁感应迭代重建方法。首先根据滤波反投影重建算法原理,给出初始电导率分布,其次基于电导率变化敏感性加权计算滤波反投影矩阵,最后利用一步牛顿迭代构成滤波反投影迭代重建算法,通过设置理想条件数G来修正Hessian矩阵,改善重建过程的病态程度,并对待重建数据进行标准化位置校正处理。实验结果表明,该算法成像速度快,重建出的图像具有较高分辨率,能够准确反映成像区域内仿真病变的大小及位置信息,且轮廓清晰,为颅脑磁感应断层成像技术应用于临床监护奠定了基础。     

基于群稀疏系数估计的图像重构算法

基于稀疏表示的图像先验信息模型被广泛用于实现图像的重构中。针对稀疏表示中字典选择与系数估计的关键问题,提出了基于稀疏表示与非局部自相似性相结合的图像重构方法。首先通过欧氏距离的块匹配寻找相似图像块,并利用左右字典分别对相似图像块集合进行局部稀疏与非局部稀疏表示,以获得更稀疏准确的稀疏表示系数。进一步针对传统阈值收缩法对稀疏系数估计精度不足的问题,利用伯格曼迭代算法快速有效地求解重构模型,并采用线性最小均方误差估计准则(LMMSE)实现稀疏系数的估计,以保证对包含图像纹理细节信息的小系数的精确估计。实验结果表明,本文方法不仅在PSNR等客观指标上达到了目前先进水平,而且重构后图像拥有更为丰富的细节信息,整体视觉效果更加清晰。     

Preconditioned Landweber iteration algorithm for electrical capacitance tomography

Electrical capacitance tomography (ECT) has been used to obtain the cross-section images of processes with different dielectric materials inside. Image reconstruction with ECT is to retrieve the permittivity distribution of materials inside the sensor from the capacitance measurements. Algorithms for ECT image reconstruction should be both precise and fast in order to satisfy the requirements of real-time monitoring of the dynamic behavior of processes. Several algorithms have been developed for ECT image reconstruction. The Landweber iteration is the most precise one. However, the low convergence rate of the Landweber iteration limits its application for on-line imaging.This paper introduces an iterative algorithm based on the Landweber method with preconditioning for ECT image reconstruction. A preconditioner, which is equivalent to a filter, is applied to the Landweber iteration. The convergence of this algorithm is analyzed. Its performance is evaluated by using simulated and experimental data corresponding to certain typical permittivity distributions. Preliminary numerical and experimental results show that this algorithm converges more rapidly than the Landweber iteration without preconditioning. Therefore, image reconstruction iteration can be accelerated, which makes on-line quantitative image reconstruction possible.     

混合高斯/泊松最大似然函数下的CBCT图像重建

如何在复杂噪声条件下提升锥形束计算机断层扫描成像(Cone Beam Computed Tomography,CBCT)图像重建质量,对于CBCT系统而言是非常重要的。本文提出了一种混合高斯/泊松最大似然函数下的CBCT图像重建方法。首先研究了适宜于描述混合高斯/泊松噪声环境下的CBCT图像重建模型,它包含一个基于混合高斯/泊松最大似然函数的保真项和一个基于三维全变分正则化方法的约束项。保真项用于约束在混合噪声模型下重建结果与观测值尽可能的相近,约束项用于噪声去除并要求尽可能较好地保留图像的边缘与细节信息。进一步通过可分离近似方法和扩展拉格朗日方法对上述模型进行求解。最后通过仿真数据和真实数据对算法的有效性进行了验证,实验结果表明:仿真结果相对于其他方法而言,PSNR最高可以提升2.1 dB;从主观视觉而言,本文方法在噪声环境下具有较好的图像重建质量。因此,本文方法可以被广泛应用于各种低剂量条件下的CBCT图像重建中。     

Shearlet regularization and dimensionality reduction for the temperature distribution sensing

In this paper, a new dimensionality reduction based temperature distribution sensing (TDS) method is proposed to reconstruct the temperature distribution via the limited number of the scattered temperature measurement data. The projective nonnegative matrix factorization (PNMF) method is developed to exact the basis vectors, and the augmented Lagrangian multipliers (ALM) method is proposed to solve the proposed PNMF model. A dimensionality reduction model is obtained via projecting the original temperature distribution onto the spaces spanned by a set of basis. An objective functional that considers the inaccurate properties of the reconstruction model and the measurement data, the Shearlet regularization and the total variation (TV) method is proposed to convert the TDS task into an optimization problem, where the temperature distribution is indirectly reconstructed via solving a low-dimensional vector. An iteration scheme is developed to solve the objective functional. Numerical simulation results validate the feasibility of the proposed reconstruction algorithm.     

Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

Compared with electrical resistance tomography, capacitively coupled electrical resistance tomography (CCERT) is preferred since it avoids problems of electrode corrosion and electrode polarization. However, reconstruction of conductivity distribution is still a great challenge for CCERT. To improve reconstruction quality, this work proposes a novel image reconstruction method based on total fractional-order variation regularization. Simulation work is conducted and reconstruction of several typical models is studied. Robustness of the proposed method to noise is also conducted. Additionally, the performance of the proposed reconstruction method is quantitatively evaluated. We have also carried out phantom experiment to further verify the effectiveness of the proposed method. The results demonstrate that the quality of reconstruction has been largely improved when compared with the images reconstructed by Landweber, Newton-Raphson and Tikhonov methods. The inclusion is more accurately reconstructed and the background is much clearer even under the impact of noise.     

3D ECT reconstruction by an improved Landweber iteration algorithm

Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. The study of a fast and accurate image reconstruction algorithm is a challenge task, especially for 3D reconstruction. In this paper, we propose an improved Landweber iteration algorithm. We incorporate an additional acceleration term into the cost function and apply an adaptive threshold operation to the image obtained in each iteration for reducing artefacts. The algorithm proposed is tested by the noise-free and noise-contaminated capacitance data. Sensitivity matrixes and capacitance data of a 3D ECT sensor are obtained by using the finite element (FE) method. Extensive simulations in 3D reconstruction are carried out. The results verify the effectiveness of these improvements. Both the reconstruction time and the artefacts in the reconstructed image are reduced obviously. The experimental results of 3D reconstruction of objects in the shape of letters U and L confirm the effectiveness of the proposed algorithm further.     

数据保真项与稀疏约束项相融合的稀疏重建

本文针对低光子计数成像过程中产生的泊松高斯混合噪声,提出了一种数据保真项与稀疏约束项相融合的稀疏重建方法。首先,基于泊松高斯噪声相互独立的混合噪声模型,建立了数据保真项与稀疏约束项相融合的稀疏重建目标函数;在图像块聚类的基础上,应用改进贪婪算法实现类内稀疏分解和字典更新;最后,稀疏分解和字典更新交替迭代求解干净图像。针对强烈泊松高斯噪声污染图像的重建实验显示,本文方法与对比方法相比,重建结果的PSNR值平均提升了5.5%,MSSIM值也有明显提升。这些结果表明:本文方法对具有强烈泊松高斯混合噪声的图像有较好的图像复原和噪声去除效果。