光学CT图象重建的数值模拟研究─Kosenbrock坐标轮换法

时间分辨光学CT技术因其对生物组织体的无损性,在生物成象领域引起了广泛的兴趣和研究,已提出许多方案,意在克服由于生物组织体中的多光散射效应所造成的成象障碍.本文简述了基于扩散方程近似的光学CT正向问题有限元解法,提出采用直接搜索优化算法-Rosenbrock坐标轮换法求解时间分辨光学CT中的图象重建问题,给出了基于积分光强和光子平均飞行时间及其加权组合的二维图象重建问题的数值模拟结果,证实了该方法的可行性.     

基于稳态辐射传输方程的小动物尺寸组织体扩散光学层析法

在Newton-Raphson逆模型框架下,发展了基于稳态辐射传输方程光子输运正模型和代数重建技术的扩散光学层析图像重建算法,并针对小尺寸组织体内部吸收和散射系数同时重建情况下的标准代数重建技术算法进行了改进,数值结果表明改进后的代数重建技术算法重建图像效果优于标准代数重建技术算法.     

基于稳态辐射传输方程的小动物尺寸组织体扩散光学层析法

在Newton-Raphson逆模型框架下,发展了基于稳态辐射传输方程光子输运正模型和代数重建技术的扩散光学层析图像重建算法,并针对小尺寸组织体内部吸收和散射系数同时重建情况下的标准代数重建技术算法进行了改进,数值结果表明改进后的代数重建技术算法重建图像效果优于标准代数重建技术算法.     

光学CT图象重建的数值模拟研究─Kosenbrock坐标轮换法

时间分辨光学 ＣＴ技术因其对生物组织体的无损性，在生物成象领域引起了广泛的兴趣和研究，已提出许多方案，意在克服由于生物组织体中的多光散射效应所造成的成象障碍．本文简述了基于扩散方程近似的光学ＣＴ正向问题有限元解法，提出采用直接搜索优化算法一Ｒｏｓｅｎｂｒｏｃｋ坐标轮换法求解时间分辨光学 ＣＴ中的图象重建问题，给出了基于积分光强和光子平均飞行时间及其加权组合的二维图象重建问题的数值模拟结果，证实了该方法的可行性．     

基于频域辐射传输模型的光学成像研究

本文以超短脉冲激光照射参与性介质的光学成像为研究背景,分别构建了短脉冲激光在参与性介质内的频域辐射传输正问题模型和根据边界探测所得频域信号重建介质内部光学参数的逆问题模型。在瞬态辐射传输方程的基础上,利用傅里叶变换得到频域辐射传输方程,采用有限体积法求解频域传输方程,模拟超短脉冲激光在二维参与性介质内传输的过程,得到介质边界的出射频域辐射信号。选取共轭梯度法作为反演算法,采用伴随差分模型求解目标函数梯度,重建了二维非均匀参与性介质内不同位置内含物的光学参数分布。结果表明,基于频域辐射传输方程的伴随差分模型能够较为准确地反演多维参与性介质内的光学参数。     

光学CT二维正向问题有限元法数值模拟的研究

简述了光学ＣＴ正模型的有限元解理论，给出了二维圆形组织体光学ＣＴ正问题的有限元法解的数值模拟结果，该结果对于生物组织体内光子传输行为以及光ＣＴ逆问题的理论和实验研究具有重要的指导意义。     

多光子成象术中深层图象损耗恢复的研究

本文在分析混浊介质对共焦三维扫描图象衰减的基础上,建立了多光子激发荧光扫描的深层图象恢复的数学模型.依照此模型对深层图象进行依赖组织光学参量的恢复,并给出了猪表皮三维扫描成象的恢复图象.     

基于傅里叶级数参数化描述的形状扩散光学层析成像方法研究

提出了一种形状扩散光学层析成像技术的数值方法,它能同时重建组织器官的边界和器官内的光学参数。假设不同类型的组织器官内具有各异的均匀光学参数,光子传输模型便可由一些耦合的Helmholtz方程描述,并使用边界元法作为正向模型的数值求解方法。区域的复杂光滑边界用傅里叶级数展开参数化方法表示。反演问题采用Levenberg-Marquardt优化算法,并用加入不同噪声水平的模拟数据对算法进行验证。数值模拟结果表明,提出的图像重建算法具有较快的收敛速度和全局收敛性,并能从噪声数据中准确恢复相应区域的形状参数和光学参数。     

基于变量分离近似稀疏重构和简化球谐近似的生物发光断层成像

生物发光断层成像(BLT)是一种非常有效的光学分子成像方式,在医学预临床研究中的有着广泛的研究。然而,BLT的核心问题即光源重建仍然存在着巨大的挑战:光在生物组织中的传输模型是否精确与重建问题不适定性都使得光源位置与密度的重建变得十分困难。为了准确高效地实现光源重建,在光传输模型的选择上,通过将扩散近似模型和高阶简化球谐近似模型(SPN)的结果与蒙特卡罗金标准进行比较,结果表明阶次(N)为3时的SP3模型描述光子在生物体的传输时能够最佳地兼顾精度和速度。基于SP3传输模型,结合光源在生物体内稀疏分布的特征,采用变量分离近似稀疏重构(SpaRSA)的方法来解决BLT的重建问题。为了验证提出方法的有效性,通过将数字鼠仿真和真实小鼠实验与典型的l1_ls方法对比表明在SP3模型下SpaRSA算法可行。     

基于序列图象对应概率累积的基础矩阵求解方法*

提出一种基于图象序列对应概率累积的基础矩阵求解算法.该算法基于序列对应概率累积求最大熵点的对应概率累积分布图,采用RANSAC思想和加权最小二乘法拟合极线,通过统计分类器求解极点并消除出格极线,并利用所求的对应极线和极点,采用一种参量化方法求解F矩阵.该算法工程实现容易,对输入的源图象没有苛刻的要求,可以基于随机拍摄的图象序列方便地求解F矩阵.     

Inverse vibration problem for un-damped 3-dimensional multi-story shear building models

Various researchers have contributed to the identification of the mass and stiffness matrices of two dimensional (2-D) shear building structural models for a given set of vibratory frequencies. The suggested methods are based on the specific characteristics of the Jacobi matrices, i.e., symmetric, tri-diagonal and semi-positive definite matrices. However, in case of three dimensional (3-D) structural models, those methods are no longer applicable, since their stiffness matrices are not tri-diagonal. In this paper the inverse problem for a special class of vibratory structural systems, i.e., 3-D shear building models, is investigated. A practical algorithm is proposed for solving the inverse eigenvalue problem for un-damped, 3-D shear buildings. The problem is addressed in two steps. First, using the target frequencies, a so-called normalized eigenvector matrix, which is a banded matrix containing the information related to the frequencies and mode shapes of the target structural system, is determined. In this regard, similar to the solution of inverse problem for 2-D shear building structural models in which an auxiliary structure is constructed by adding constraints (or springs) to the original system, three auxiliary structures are proposed to solve the problem for 3-D cases. In the second step, the normalized eigenvector matrix is utilized to obtain the normalized stiffness matrix; in turn, this matrix is decomposed into the stiffness and mass matrices of the system. Finally, a numerical example is presented to demonstrate the efficiency of the proposed algorithm in determining the mass and stiffness matrices of a 3-D structural model for a given set of target vibrational frequencies.     

基于迭代Tikhonov正规化的三刺激值重建光谱方法研究

光谱图像中的反射率光谱数据维数高,且与光源、设备均无关,能够比较全面、真实、客观地描述图像中物体的颜色信息。针对三色相机的光谱图像获取系统中三维色度数据重建多维光谱数据产生的光谱信息丢失、以及伴随而生的颜色信息丢失问题,提出了迭代Tikhonov正规化的光谱重建方法。首先依据色度学理论中色度值与反射率光谱之间的关系,构建反射率光谱重建方程建立起相机所获三维色度数据与高维反射率光谱数据的映射关系;然后,通过反射率光谱重建方程的病态分析,在Moore-Penrose伪逆矩阵求解思想的基础上构建迭代Tikhonov正规化方法求解反射率光谱,并利用训练样本数据通过L-曲线方法训练获取迭代Tikhonov正规化的最优正规化参数,以有效控制并改善反射率光谱重建方程求解的病态、减少重建光谱的光谱信息丢失。实验通过选取样本数据对光谱重建方法进行验证。验证实验的结果表明所提出的光谱重建方法改善了三色相机的光谱图像获取系统中重建光谱的光谱信息丢失程度,使得重建光谱的光谱误差和色度误差较其他光谱重建方法均有明显降低。     

Image quality improvement in ultrasonic nondestructive testing by the maximum entropy method

We consider the possibility of solving the inverse scattering problem in the linear approximation (in the form of a convolution equation) by reducing it to a system of linear algebraic equations and minimizing the residual. Since the problem is an ill-posed one, the Tikhonov regularization proves useful. The possibility of using the entropy of the image estimate as a stabilizing functional is considered, which is the key idea of the maximum entropy method. The single-frequency and multifrequency versions of the method are realized. The advantage of the maximum entropy method over the conventional linear methods of solving the inverse scattering problem is shown. The superresolution and sidelobe suppression abilities of the maximum entropy method are demonstrated. The method is shown to be stable to measurement noise and multiplicative interference in the form of aperture decimation. Examples of the image reconstruction by the maximum entropy method from model and experimental data are presented.     

非线性体效应的逆问题

非线性体效应在计算机断层扫描重构的理论与应用中,仍然是一个悬而未决的问题。本文利用优化重构算法来解决非线性体效应,建立了离散非线性X射线投影变换模型,将X射线投影变换的逆问题变为一个非凸的优化问题,通过裁剪原用于求解凸优化问题的一阶原对偶算法,提出非线性迭代重构算法来解决非凸问题。通过构建成像系统模型对算法的收敛性和重构精度进行了模拟验证,模拟中重建的图像首先通过检验在极窄的显示窗口中显示小于1%的对比度,然后使用相对于真实图像的差异的l2范数进行定量分析。模拟结果表明,在计算精度范围内,非线性重构算法可以收敛到真实图像,而且对于图像衬度小于1%的细节,重构图像也可以显示出来。该研究结果可为设计有效补偿非线性体效应伪影的计算机断层扫描成像应用算法提供参考。     

基于非线性光谱字典学习的单幅RGB图像光谱重建方法研究

针对单幅RGB图像重建光谱图像中的病态问题,提出一种基于非线性光谱字典学习的非线性重建方法。为了适应线性和非线性数据,该方法首先改进了基于自联想神经网络模型的非线性主成分分析算法,并利用其从训练光谱集中学习低维光谱字典,用于光谱重建的求逆方程中,以缓解病态状况。再在此光谱字典基础上,利用阻尼高斯牛顿法结合截断奇异值分解的正则化方法,进一步缓解该非线性反演的病态问题,实现单幅RGB图像重建光谱图像。在实验中,采用Munsell以及Munsell+Pantone两个光谱训练集学习光谱字典,同时利用CAVE和UEA光谱图像库进行光谱重建测试。该方法测试结果与现有方法比较发现,该方法在不同光谱训练集下重建CAVE和UEA两库光谱图像的均方根差的平均值最低,分别为0.212 4,0.255 4,0.229 4和0.294 9,均方根差的标准偏差接近最好方法的效果,分别为0.068 5,0.084 7,0.066 8和0.087 0。此结果表明该方法针对单幅RGB图像重建光谱图像在重建精度和稳定性上均存在优势。     

On the possibility of using the maximum entropy method in ultrasonic nondestructive testing for scatterer visualization from a set of echo signals

We have studied the possibility of solving the inverse scattering problem in the Born approximation, i.e., the reconstruction of scatterer images from the measured set of echo signals. We have considered generalization of the classical combined SAFT (C-SAFT) algorithm to the case of multiple reflections from uneven boundaries of the tested object taking into account the transformation of the wave type for several positions of the antenna grid, which makes it possible to obtain high-quality scatterer images. Representation of the direct problem in matrix form makes it possible to switch to solving the inverse problem, which can be solved using the Tikhonov regularization procedure, because it is an ill-posed. We have considered the possibility of using the entropy of the image estimate as the stabilizing functional that forms the essence of the maximum entropy method (MEM). The advantage of the MEM over the conventionally used linear C-SAFT method has been shown. The ray model taking into account reflections of rays from the boundaries of the tested object with uneven boundaries has been used for constructing the function estimate. We have demonstrated the ability of the MEM to obtain the scatterer images with superresolution and to suppress the “side lobes” of the function of the point scattering on the collapsed set of echo signals. The use of echo signals reflected from the boundaries of the tested object makes it possible to reconstruct the scatterer shape more exactly. Examples of images reconstructed by the MEM on echo signals obtained in the numerical and model experiments have been presented.     

Sparsity-constrained SENSE reconstruction: An efficient implementation using a fast composite splitting algorithm

Parallel imaging and compressed sensing have been arguably the most successful and widely used techniques for fast magnetic resonance imaging (MRI). Recent studies have shown that the combination of these two techniques is useful for solving the inverse problem of recovering the image from highly under-sampled k-space data. In sparsity-enforced sensitivity encoding (SENSE) reconstruction, the optimization problem involves data fidelity (L2-norm) constraint and a number of L1-norm regularization terms (i.e. total variation or TV, and L1 norm). This makes the optimization problem difficult to solve due to the non-smooth nature of the regularization terms. In this paper, to effectively solve the sparsity-regularized SENSE reconstruction, we utilize a new optimization method, called fast composite splitting algorithm (FCSA), which was developed for compressed sensing MRI. By using a combination of variable splitting and operator splitting techniques, the FCSA algorithm decouples the large optimization problem into TV and L1 sub-problems, which are then, solved efficiently using existing fast methods. The operator splitting separates the smooth terms from the non-smooth terms, so that both terms are treated in an efficient manner. The final solution to the SENSE reconstruction is obtained by weighted solutions to the sub-problems through an iterative optimization procedure. The FCSA-based parallel MRI technique is tested on MR brain image reconstructions at various acceleration rates and with different sampling trajectories. The results indicate that, for sparsity-regularized SENSE reconstruction, the FCSA-based method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction method.     

基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高,无法满足实时系统要求的缺陷,提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化,采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数,保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度,且对噪声图像也能实现高效的分割。     

Dual Principles in Maximum Entropy Reconstruction of the Wave Distribution Function

Maximum entropy methods are used for reconstructing the distribution of energy in wave vector space from frequency spectra observed on board satellites. The reconstruction scheme is based on a modified entropy function, and dual principles are used to solve the resulting optimization problem. Our scheme is not limited to reconstructions of wave distribution functions, but it should be useful also for solving other types of underdetermined inverse problems.     

Optical tomography using the time-independent equation of radiative transfer—Part 2: inverse model

Optical tomography is a novel imaging modality that is employed to reconstruct cross-sectional images of the optical properties of highly scattering media given measurements performed on the surface of the medium. Recent advances in this field have mainly been driven by biomedical applications in which near-infrared light is used for transillumination and reflectance measurements of highly scattering biological tissues. Many of the reconstruction algorithms currently utilized for optical tomography make use of model-based iterative image reconstruction (MOBIIR) schemes. The imaging problem is formulated as an optimization problem, in which an objective function is minimized. In the simplest case the objective function is a normalized-squared error between measured and predicted data. The predicted data are obtained by using a forward model that describes light propagation in the scattering medium given a certain distribution of optical properties.In part I of this two-part study, we presented a forward model that is based on the time-independent equation of radiative transfer. Using experimental data we showed that this transport-theory-based forward model can accurately predict light propagation in highly scattering media that contain void-like inclusions. In part II we focus on the details of our image reconstruction scheme (inverse model). A crucial component of this scheme involves the efficient and accurate determination of the gradient of the objective function with respect to all optical properties. This calculation is performed using an adjoint differentiation algorithm that allows for fast calculation of this gradient. Having calculated this gradient, we minimize the objective function with a gradient-based optimization method, which results in the reconstruction of the spatial distribution of scattering and absorption coefficients inside the medium. In addition to presenting the mathematical and numerical background of our code, we present reconstruction results based on experimentally obtained data from highly scattering media that contain void-like regions. These types of media play an important role in optical tomographic imaging of the human brain and joints.