采用贝叶斯决策概率型DT-MRI的脑白质纤维追踪成像

弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像可无创重建脑白质纤维的三维结构,而现有追踪成像方法一般仅考虑局部纤维的弥散倾向,对纤维束几何结构的综合考虑不足,为此提出一种贝叶斯决策概率型的纤维追踪成像算法.该算法通过纤维束当前体素的弥散张量方向和纤维束几何结构信息,利用贝叶斯决策理论估算追踪下一体素的方向概率分布;按照概率分布对纤维束进行加权采样,重建纤维束的三维结构图像.最后利用文中算法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验.仿真和实验结果表明,该算法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强.     

基于路径跟踪控制方法的拖挂式机器人系统路径规划算法

为解决拖挂式移动机器人系统路径规划算法精准性低、稳定性差和无法考虑系统间安全性等的问题,提出一种基于路径跟踪方法的路径规划算法。该算法融合快速拓展随机树（RRT）基本算法和路径跟踪控制方程,通过自动拟合样条曲线,跟踪并生成节点间轨迹,以此提高路径精准性;加入系统夹角约束条件和节点击中机制提高算法稳定性和结果安全性;此外,加入贪心优化算法,针对结果路径进行优化处理。通过仿真实验结果表明,相较基本RRT算法,改进算法搜索得到的路径更贴近实际运动轨迹,在安全性和成功率上优于原算法,能够满足快速设计或实时系统的需求。     

多目标跟踪算法的研究与应用

将多目标跟踪算法用于水中絮体颗粒的跟踪,提出了一种代价函数结合改进MHT的算法,对出现异常的絮体颗粒用改进MHT算法跟踪,并通过分析絮体颗粒运动特性修正了改进MHT算法,有效解决了跟踪过程中出现的轨迹交叉、轨迹合并等情况,这样在保证跟踪精度的基础上又提高了跟踪速度;同时,采用跟踪絮体颗粒得到颗粒的沉淀速度信息来评价水处理混凝的效果,自动控制混凝剂的加注,从而达到自来水水质检测和净化的目的。     

全局脑白质纤维群智能跟踪算法

为了解决概率纤维跟踪算法"过度"误跟踪,效率低的缺点,受蚁群路径搜索过程群体协作模式启发,提出一种全局脑白质纤维群体智能跟踪方法。首先,构建了一种全局纤维度量指标,综合考虑局部纤维方向分布和全局纤维走向,并利用贝叶斯方法建立局部纤维方向分布不确定信息模型。其次,提出一种群智能全局优化算法。该算法构建基于von Miser-fisher分布函数的信息素模型,通过信息素模型诱导迭代优化纤维轨迹。人工合成数据实验结果表明,跟概率跟踪算法相比,该算法解决了纤维局部误差积累导致的误跟踪问题,相对误差降低至原来的二分之一,计算规模降低至原来的十分之一。实际临床数据验证了算法的有效性。     

基于QPF的无源传感器目标跟踪算法

在无源传感器目标跟踪系统的研究中,在双红外传感器组成的无源传感器目标跟踪系统模型中,传感器提供的仅是目标的角度信息,导致量测值与目标状态之间存在较强的非线性关系,而传统跟踪算法在解决非线性问题时具有局限性,为提高目标跟踪精度,提出一种基于积分粒子滤波(QPF)的无源传感器目标跟踪算法,在不受非线性、非高斯问题限制的粒子滤波的基础上,从改进粒子滤波重要性函数的角度入手,利用积分卡尔曼滤波(QKF)将当前最新量测考虑在内,构造出粒子滤波的重要性函数,使得改进后的重要性函数更加贴近真实后验分布,从而有效遏制了粒子退化现象.仿真结果表明,改进算法提高了跟踪精度,较好地解决了无源传感器对目标的非线性跟踪优化问题.     

基于启发式算法的路径搜索

A*算法是人工智能中一种典型的启发式搜索算法,它在游戏开发、机器人导航等领域内得到广泛的应用.本文在A*算法的基础上进行改进,将其应用到大规模行人仿真系统中,取得良好效果.由于采用不同的启发函数能够得到不同的路径,本文对几种启发函数进行了分析、比较和改进,用以模拟行人选择路径的多样化,通过对地图进行预处理,对靠近障碍物节点的增加惩罚值,解决了传统A*算法搜索得到的路径过于"贴边"问题;提出了一种"修剪算法".使得经过处理的路径更加真实,更加符合行人行走的习惯.     

基于免疫匹配追踪的语音稀疏分解算法

针对标准匹配追踪(MP)算法在寻找最佳原子时计算量大的问题,提出一种基于免疫匹配追踪(IA-MP)的语音稀疏分解算法。该算法采用免疫克隆优化机制搜索最佳原子,利用抗体的种群规模控制冗余字典的大小,选择实数交叉与非均匀变异方法保证字典的完备性。仿真实验结果表明,与标准MP算法和遗传匹配算法相比,IA-MP算法可明显降低匹配追踪的计算量,算法性能较稳定,利用该算法分解后的稀疏信号具有较高的重构精度。     

基于阈值的脑白质纤维概率跟踪算法

概率跟踪算法仅对行走概率最大的方向进行跟踪,忽略了纤维走向概率较大的方向,且运算速度较慢。为此,提出一种基于阈值的快速概率跟踪算法。设定纤维走向的概率阈值,以找到更多交叉和分叉的纤维,在不影响纤维跟踪效果的情况下,对计算参数进行简化,从而提高运算速度。实验结果表明,与概率跟踪算法相比,该算法能更好地反映脑白质内神经纤维的分布情况,且缩短运算时间。     

基于多路线追踪的机器人局部路径规划与实验

为了提高智能送餐机器人局部路径规划的稳定性和实用性,针对在机器人初始姿态与预设路径偏差较大时使用传统纯追踪模型受限的问题,使用多次追踪的方式对传统纯追踪模型算法进行改进。通过建立机器人简化模型,设计多条具有相切几何关系的追踪路线,达到减小前视距离取值的目的,解决了因前视距离取值不当引起的追踪抖动和追踪拟合程度下降的问题,并通过仿真和实验分析,验证了改进后算法的适用性和高效性。     

基于复位控制的多智能体系统跟踪控制

针对多智能体系统的目标跟踪控制问题,提出了一种基于复位控制策略的分布式目标跟踪算法。该算法是由传统的比例积分比较器和克莱格积分器组成,极大地提高了多智能体系统的跟踪速度。首先,在传统静态反馈控制算法的基础上,加入克莱格积分器作为复位项,构成复位控制算法;然后,通过构造Lyapunov函数证明了该闭环系统的稳定性,并定量地比较了传统静态算法和复位算法下的目标追踪速度;最后,通过仿真实例证明了所提基于复位控制策略的目标跟踪算法的有效性。     

Distance-based tractography in high angular resolution diffusion MRI

High angular resolution diffusion imaging (HARDI) is a magnetic resonance imaging (MRI) technique, determining the diffusion of water molecules in tissue in vivo. HARDI is advantageous over the well-known diffusion tensor imaging (DTI), since it is able to extract more than one fiber orientation within a voxel and can therefore resolve crossing, kissing or fanning fiber tracts. However, multiple orientations per voxel require more sophisticated tractography approaches. In this paper we introduce a new deterministic fiber tracking method using the complete orientation distribution function (ODF) reconstructed from Q-ball imaging to enable tractography in challenging regions. Anisotropy classifiers are used to differentiate intra-voxel fiber populations and adjust a curvature threshold for one and multiple fiber configurations, respectively. In addition, we determine the most appropriate propagation direction in complex white matter regions, using the course of the current tract. To ensure tractography running within fiber bundles, a distance-based approach is integrated, which aims to maintain the initial distance of the seed point to the white matter boundary through the whole tracking. We evaluated our method using a phantom dataset featuring crossing, kissing and fanning fiber configurations and a human brain dataset, reconstructing the fanning of the corpus callosum and considering the region of the centrum semiovale.     

三维脑白质纤维束成像方法?

从Riemannian流形的角度分析扩散张量成像,将脑白质中任意两点间的纤维束生成问题转化为计算Riemannian流形中两点间测地线的问题,通过Level-Set方法计算测地线,并将其作为脑白质中两点间的纤维束。利用模拟脑白质纤维束对该算法和传统算法进行比较,实验结果表明,该算法在准确性、鲁棒性等方面有较大改进。     

神经纤维跟踪算法研究进展

神经纤维跟踪通过整合纤维局部结构方向信息,可以描绘出具有解剖学意义的空间纤维结构,是扩散磁共振成像的关键步骤,对临床医学与神经科学等有着重大意义。然而,大量的研究和临床应用表明,目前的神经纤维跟踪算法重构出了大量虚假纤维而备受质疑。为了给研究者和临床医生选择神经纤维跟踪算法提供依据,本文深入分析了当前的主要跟踪算法并进行定量评估与定性比较。从确定型、概率型和全局优化等方法详细介绍各典型跟踪算法;利用Fibercup和国际医学磁共振学会（International Society for Magnetic Resonance in Medicine,ISMRM）2015挑战数据进行实验,定量对比9种常用算法的优缺点,并分析了这些算法在实际临床数据的成像结果及其面临的挑战;结合实验结果与算法理论分析各算法的内在联系与区别。不同跟踪算法在效果上有着较大的差异,确定型算法在描绘主要纤维结构上更为明显,概率型算法描绘的纤维分布更为全面,全局优化算法的纤维轨迹更符合全局数据而避免了误差累积问题。纤维跟踪对于分析人脑神经纤维连接具有很高的研究价值和应用价值。不同类型的算法有着各自的优缺点,目前并没有一种跟踪算法可以摒弃其他算法缺点而结合所有优点。另外目前纤维跟踪算法的结果与实际情况均有着一定差距,如何描绘出更为精确的纤维轨迹仍是一个具有挑战性的问题。     

Finsler Streamline Tracking with Single Tensor Orientation Distribution Function for High Angular Resolution Diffusion Imaging

We introduce a new framework based on Riemann-Finsler geometry for the analysis of 3D images with spherical codomain, more precisely, for which each voxel contains a set of directional measurements represented as samples on the unit sphere (antipodal points identified). The application we consider here is in medical imaging, notably in High Angular Resolution Diffusion Imaging (HARDI), but the methods are general and can be applied also in other contexts, such as material science, et cetera, whenever direction dependent quantities are relevant. Finding neural axons in human brain white matter is of significant importance in understanding human neurophysiology, and the possibility to extract them from a HARDI image has a potentially major impact on clinical practice, such as in neuronavigation, deep brain stimulation, et cetera. In this paper we introduce a novel fiber tracking method which is a generalization of the streamline tracking used extensively in Diffusion Tensor Imaging (DTI). This method is capable of finding intersecting fibers in voxels with complex diffusion profiles, and does not involve solving extrema of these profiles. We also introduce a single tensor representation for the orientation distribution function (ODF) to model the probability that a vector corresponds to a tangent of a fiber. The single tensor representation is chosen because it allows a natural choice of Finsler norm as well as regularization via the Laplace-Beltrami operator. In addition we define a new connectivity measure for HARDI-curves to filter the most prominent fiber candidates. We show some very promising results on both synthetic and real data.     

Estimating crossing fibers: a tensor decomposition approach

Diffusion weighted magnetic resonance imaging is a unique tool for non-invasive investigation of major nerve fiber tracts. Since the popular diffusion tensor (DT-MRI) model is limited to voxels with a single fiber direction, a number of high angular resolution techniques have been proposed to provide information about more diverse fiber distributions. Two such approaches are Q-Ball imaging and spherical deconvolution, which produce orientation distribution functions (ODFs) on the sphere. For analysis and visualization, the maxima of these functions have been used as principal directions, even though the results are known to be biased in case of crossing fiber tracts. In this paper, we present a more reliable technique for extracting discrete orientations from continuous ODFs, which is based on decomposing their higher-order tensor representation into an isotropic component, several rank-1 terms, and a small residual. Comparing to ground truth in synthetic data shows that the novel method reduces bias and reliably reconstructs crossing fibers which are not resolved as individual maxima in the ODF. We present results on both Q-Ball and spherical deconvolution data and demonstrate that the estimated directions allow for plausible fiber tracking in a real data set.     

Compact implicit surface reconstruction via low-rank tensor approximation

Implicit representations have gained an increasing popularity in geometric modeling and computer graphics due to their ability to represent shapes with complicated geometry and topology. However, the storage requirement, e.g. memory or disk usage, for implicit representations of complex models is relatively large. In this paper, we propose a compact representation for multilevel rational algebraic spline (MRAS) surfaces using low-rank tensor approximation technique, and exploit its applications in surface reconstruction. Given a set of 3D points equipped with oriented normals, we first fit them with an algebraic spline surface defined on a box that bounds the point cloud. We split the bounding box into eight sub-cells if the fitting error is greater than a given threshold. Then for each sub-cell over which the fitting error is greater than the threshold, an offset function represented by an algebraic spline function of low rank is computed by locally solving a convex optimization problem. An algorithm is presented to solve the optimization problem based on the alternating direction method of multipliers (ADMM) and the CANDECOMP/PARAFAC (CP) decomposition of tensors. The procedure is recursively performed until a certain accuracy is achieved. To ensure the global continuity of the MRAS surface, quadratic B-spline weight functions are used to blend the offset functions. Numerous experiments show that our approach can greatly reduce the storage of the reconstructed implicit surface while preserve the fitting accuracy compared with the state-of-the-art methods. Furthermore, our method has good adaptability and is able to produce reconstruction results with high quality.     

基于三次B样条曲线拟合的智能车轨迹跟踪算法

针对传统几何轨迹跟踪算法切向角获取依赖高精度惯导设备的问题，提出了基于三次B样条曲线拟合的轨迹跟踪算法。首先，通过对先验地图中的离散轨迹点进行拟合生成平滑轨迹线；然后，根据轨迹方程通过插值法重新生成离散路点，并计算各个路点处的切向角，从而实现了对多传感器融合轨迹的优化与跟踪。在真实的智能车实验平台上，用所提算法对20km/h低速绕圈和60km/h较高速度直道两种典型场景进行了在真实道路下的跟踪测试。在低速大曲率和较高速度直道两种典型场景下，所提算法轨迹跟踪的最大横向误差均保持在0.3m以内。实验结果表明，该算法有效解决了传统几何轨迹跟踪算法对惯导设备依赖的问题，同时保持了较好的跟踪性能。     

基于SLPP和张量分解相结合的人脸识别*

针对多线性分析算法对多姿态多身份因素并存时,人脸的识别率大大下降等问题,提出了带监督的局 部保留投影映射算法与多线性张量分析算法相结合的人脸识别方法。该方法将人脸转动的近邻点信息作为监 督信息引入,更精确地描述了姿态空间的非线性结构,再结合张量分解和核函数将姿态流形系数映射到高维图 像空间,使得从低维空间到高维空间映射的精确性得以提高。在东方人脸数据库上进行实验,结果验证了该算 法的有效性。     

Nonnegative Diffusion Orientation Distribution Function

Because of the well-known limitations of diffusion tensor imaging (DTI) in regions of low anisotropy and multiple fiber crossing, high angular resolution diffusion imaging (HARDI) and Q-Ball Imaging (QBI) are used to estimate the probability density function (PDF) of the average spin displacement of water molecules. In particular, QBI is used to obtain the diffusion orientation distribution function (ODF) of these multiple fiber crossing. As a probability distribution function, the orientation distribution function should be nonnegative which is not guaranteed in the existing methods. This paper proposes a novel technique to guarantee the nonnegative property of ODF by solving a convex optimization problem, which has a convex quadratic objective function and a constraint involving the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity tensor. Using convex analysis and optimization techniques, we first derive the optimality conditions of this convex optimization problem. Then, we propose a gradient descent algorithm to solve this problem. We also present formulas for determining the principal directions (maxima) of the ODF. Numerical examples on synthetic data as well as MRI data are displayed to demonstrate the significance of our approach.     

DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking

A versatile resource program was developed for diffusion tensor image (DTI) computation and fiber tracking. The software can read data formats from a variety of MR scanners. Tensor calculation is performed by solving an over-determined linear equation system using least square fitting. Various types of map data, such as tensor elements, eigenvalues, eigenvectors, diffusion anisotropy, diffusion constants, and color-coded orientations can be calculated. The results are visualized interactively in orthogonal views and in three-dimensional mode. Three-dimensional tract reconstruction is based on the Fiber Assignment by Continuous Tracking (FACT) algorithm and a brute-force reconstruction approach. To improve the time and memory efficiency, a rapid algorithm to perform the FACT is adopted. An index matrix for the fiber data is introduced to facilitate various types of fiber bundles selection based on approaches employing multiple regions of interest (ROIs). The program is developed using C++ and OpenGL on a Windows platform.