Diffusion tensor imaging fiber tracking with local tissue property sensitivity: phantom and in vivo validation

Diffusion tensor imaging (DTI) provides directional information that can be used to delineate brain white matter connections noninvasively via fiber tracking. The most commonly used methods for tractography are based on the streamline tracking algorithm for track propagation and a set of empirically and globally defined criteria for track termination. In this study, we propose a streamline tracking algorithm with high-order propagation accuracy and a single termination criterion based on tissue property to minimize user intervention and biases introduced during tracking process. These advantages and the agreement with histological reports are demonstrated in our tracking results in phantoms and in humans.     

A survey of spectrogram track detection algorithms

The detection of tracks in spectrograms is an important step in remote sensing applications such as the analysis of marine mammal calls and remote sensing data in underwater environments. Recent advances in technology and the abundance of data requires the development of more sensitive detection methods. This problem has attracted researchers’ interest from a variety of backgrounds ranging between image processing, signal processing, simulated annealing and Bayesian filtering. Most of the literature is concentrated in three areas: image processing, neural networks, and statistical models such as the Hidden Markov model. There has not been a review paper which describes and critically analyses the application of these key algorithms. This paper presents an extensive survey and an algorithm taxonomy, additionally each algorithm is reviewed according to a set of criteria relating to their success in application. These criteria are defined to be their ability to cope with noise variation over time, track association, high variability in track shape, closely separated tracks, multiple tracks, the birth/death of tracks, low signal-to-noise ratios, that they have no a priori assumption of track shape and that they are computationally cheap. Our analysis concludes that none of these algorithms fully meets these criteria.     

Localisation of moving sources in a multipath propagation environment

A new algorithm is presented for tracking correlated narrow-band sources in the presence of colored Gaussian noise. A fast cumulant-based preprocessing method is used to remove unknown noise and a Kalman filtering is used to track the source parameters. The use of a Kalman filtering avoids the data association problem and improves the tracking performance for crossing tracks. It is applied to the outputs of Newton’s algorithm to track moving sources. In this paper, the algorithm is developed for the special case in which the updated cumulant matrix is obtained by substituting a new matrix of the current data. The rank tracking problem is not considered in this study.We demonstrate the performance of the proposed algorithm by computer simulations of the tracking of moving targets emitting correlated signals, we also tested the proposed algorithm on the real data recorded during an underwater acoustic experiments.     

Pattern-Matching Track Reconstruction for the BESⅢ Main Drift Chamber

The tracking algorithm, MdcPatRec, is developed for the Drift Chamber of the BESⅢ detector using C++ language and object-oriented techniques. This algorithm uses a pattern matching method to find track segments and then combine them into track candidates followed by a least square fit. With the simulation data, the tracking performances such as efficiencies and momentum resolution have been studied and results are consistent with parameters from detector design. The algorithm is also proved to be robust enough to process data with severe background expected by the BESⅢ experiment.     

Sequential trans-dimensional Monte Carlo for range-dependent geoacoustic inversion

This paper develops a sequential trans-dimensional Monte Carlo algorithm for geoacoustic inversion in a strongly range-dependent environment. The algorithm applies advanced Markov chain Monte Carlo methods in combination with sequential techniques (particle filters) to carry out geoacoustic inversions for consecutive data sets acquired along a track. Changes in model parametrization along the track (e.g., number of sediment layers) are accounted for with trans-dimensional partition modeling, which intrinsically determines the amount of structure supported by the data information content. Challenging issues of rapid environmental change between consecutive data sets and high information content (peaked likelihood) are addressed by bridging distributions implemented using annealed importance sampling. This provides an efficient method to locate high-likelihood regions for new data which are distant and ∕ or disjoint from previous high-likelihood regions. The algorithm is applied to simulated reflection-coefficient data along a track, such as can be collected using a towed array close to the seabed. The simulated environment varies rapidly along the track, with changes in the number of layers, layer thicknesses, and geoacoustic parameters within layers. In addition, the seabed contains a geologic fault, where all layers are offset abruptly, and an erosional channel. Changes in noise level are also considered.     

Parallel re-initialization of level set functions on distributed unstructured tetrahedral grids

Level set functions are employed to track interfaces in various application areas including simulation of two-phase flows and image segmentation. Often, a re-initializing algorithm is incorporated to transform a numerically instable level set function to a signed distance function. In this note, we present a parallel algorithm for re-initializing level set functions on unstructured, three-dimensional tetrahedral grids. The main idea behind this new domain decomposition approach is to combine a parallel brute-force re-initializing algorithm with an efficient way to compute distances between the interface and grid points. Time complexity and error analysis of the algorithm are investigated. Detailed numerical experiments demonstrate the accuracy and scalability on up to 128 processes.     

多光谱目标探测的波段选择

利用超光谱数据对多光谱传感器的波段选择提出了一个框架。根据探测理论,导出了一个代价函数,对于区分弱小目标和杂波(光谱分布已知)的探测性能最优的光谱波段来说,可使该函数最小。利用匹配追踪算法找到了一个近似最小的代价函数,从而选出了滤波器。该方法可用于设计多光谱红外搜索跟踪和光电导弹预警传感器。在杂波背景下,对小目标探测的低虚警率和高探测概率来说是至关重要的。     

Mean-shift跟踪算法及其在光电跟踪系统中的应用

针对光电跟踪系统中实时提取运动目标脱靶量的应用需求,设计了一种基于灰度直方图的Mean-shift 图像跟踪算法,对算法中目标模型与候选模型的建立进行了改进,抑制了背景像素对目标跟踪产生的影响。算法在系统上位机Visual C+ + 6.0平台上实现,当光电跟踪系统捕获到运动目标后,利用Mean-shift图像跟踪算法跟踪运动目标,并实时将运动目标脱靶量作为伺服控制系统的输入信号,驱动跟踪器跟踪目标。实验结果表明：设计的算法可以实时、准确、有效地跟踪运动目标,使稳定后的脱靶量换算得到的角偏差量控制在30"之内。     

月球目标的探测与跟踪技术研究

研究了月球目标探测成像及自动跟踪系统。利用CCD图像传感器、图像处理技术以及图像跟踪算法可以取代对月球的手动跟踪,无需预知月球的运动轨道,在自动跟踪的同时记录月球影像,便于对月球表面数据的记录和处理,并能进行实时图像处理,利于对月球图像的分析,为月球探测跟踪做了一次有益的新方法尝试。     

General expression of the calibration curve for the CR-39 radon track detectors

The CR-39 detectors are widely used as passive radon dosimeters, as well as in physics laboratories or for industrial applications. For what concerns radon monitoring, the calibration curve which is usually adopted corresponds to a linear relation between the actual etched track density and the track density counted by an automatic acquisition system. This linear calibration provides very accurate radon exposure assessments in a restricted range of etched track density, however it neglects the effect of the tracks overlapping that becomes as relevant as the track density increases. In the present work the mathematical expression of the area covered by a set of uniformly distributed tracks is deduced. This result allows then to infer the probability that the system acquires the right track density, providing a general calibration curve for a solid state radon track detector. The process of track production and reading routine is also simulated adopting a Monte Carlo approach, showing that the obtained results are in agreement with the function proposed as calibration curve. Moreover, a linear dependence between the track average area and the rate parameter of the calibration curve has been observed. Finally a semi-empirical correlation based on the previous results is proposed.     

复杂背景下相关跟踪算法的研究

针对传统跟踪算法（如邻域法、传统的相关跟踪算法）只能跟踪海上或空中目标，不能跟踪复杂背景下目标的问题，提出了一种稳定快速跟踪复杂背景下目标的算法。该算法在传统相关跟踪算法的基础上进行改进，相关处理之前采用边缘检测、阈值分割等方法去除复杂背景，提取出具有特征的目标信息。在跟踪过程中，根据匹配的效果自动对模板进行刷新。给出了算法实现的硬件组成和程序流程图。实验证明对于传统算法无法稳定跟踪的目标，改进后的算法能够实现稳定跟踪。     

Track Reconstruction in Vertex Chamber of BES-Ⅱ

This paper describes the algorithm of the non-numerical pattern recognition used in the VC of BES-Ⅱ. The code is good to provide the spacial resolution and the effeciency of the track found by M.C. data and experimental data.     

A novel algorithm for the reduction of undersampling artefacts in magnetic resonance images

An innovative algorithm is presented which is effective in reducing the truncation artefacts occurring in magnetic resonance images due to missing k-space samples. The algorithm works first by filling the incomplete matrix of coefficients with zeroes and then adjusting, by an iterative process, the missing coefficients by performing a reduction of the undersampling artefacts. Then, this set of coefficients is used as a basis for a superresolution algorithm that estimates the missing coefficients by modeling the data as a linear combination of increasing and decreasing exponential functions using Prony's method. In fact, the Prony's method consists of the interpolation of a given data set with a sum of exponential functions: the MRI signals can be well represented as a sum of exponential functions and the missing data can be extrapolated by this representation. The algorithm has been proven to perform better than either a simple algorithm, which detects and then reduces the undersampling artefacts, or an algorithm that models the measured data with approximation functions. The presented algorithm is quite simple and is applicable both to missing rows (phase-frequency acquisitions) and to radial-missing angle (acquisition from projections) undersampling. Experimental results are reported; comparisons, made between the results obtained using the presented algorithm and the alternative methods described above, clearly demonstrate the superiority of the algorithm.     

红外鱼眼系统下的多目标航迹起始方法研究

为了实现红外鱼眼系统下对多目标航迹进行起始,分析了红外鱼眼系统下目标在像面上的运动特点,提出了用于红外鱼眼系统的多目标航迹起始算法。算法采用逻辑法中的3/4逻辑,利用最大速度约束法去除各采样周期中大部分非相关量测点,用Hough变换方法确定可能航迹,用匀速约束对航迹进行确认。利用模拟目标对算法进行了实验和分析,结果表明,该算法与标准Hough变换法和逻辑法相比,航迹起始性能有了明显提高。当杂波个数为450个时,航迹起始虚警率仅为14%;对非断点航迹的成功起始时间为3帧,对断点航迹的成功起始时间为4帧,且在密集杂波环境下算法执行时间也较短。     

Unwrapping magnetic resonance phase maps with Chebyshev polynomials

A phase-unwrapping algorithm, based on the method of moments, is introduced in this work. The proposed algorithm expands the phase map in terms of a two-dimensional Chebyshev series. The expansion coefficients are calculated by exploiting the orthogonality of Chebyshev polynomials of the first kind. The performance of the proposed phase-unwrapping algorithm is tested on a synthetic phase map and experimental phase maps of a uniform phantom, a human brain and a mouse torso, all acquired from 3-T magnetic resonance (MR) scanners. To impose additional burdens on the algorithm, we introduced magnetic field inhomogeneities to the phantom and human brain data by an external gradient coil. The proposed phase-unwrapping algorithm is found to perform well on the phantom data set in a low signal-to-noise ratio (SNR) environment and on the synthetic data set. The proposed algorithm is also found to perform well in in vivo data sets of the human brain and mouse torso. Results obtained from the in vivo MR data sets show that the proposed algorithm produced unwrapped phase maps that are nearly identical to those produced by a widely used phase-unwrapping algorithm, PRELUDE 2D in the fMRI Software Library.     

A precise integration method for solving coupled vehicle–track dynamics with nonlinear wheel–rail contact

A new method is proposed as a solution for the large-scale coupled vehicle–track dynamic model with nonlinear wheel–rail contact. The vehicle is simplified as a multi-rigid-body model, and the track is treated as a three-layer beam model. In the track model, the rail is assumed to be an Euler-Bernoulli beam supported by discrete sleepers. The vehicle model and the track model are coupled using Hertzian nonlinear contact theory, and the contact forces of the vehicle subsystem and the track subsystem are approximated by the Lagrange interpolation polynomial. The response of the large-scale coupled vehicle–track model is calculated using the precise integration method. A more efficient algorithm based on the periodic property of the track is applied to calculate the exponential matrix and certain matrices related to the solution of the track subsystem. Numerical examples demonstrate the computational accuracy and efficiency of the proposed method.     

Low-field magnetic resonance imaging using multiplicative regularization

In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio.     

基于优选样本的KPCA高光谱图像降维方法

 降维是高光谱图像常用的预处理手段,而核主成份分析通过非线性映射能够挖掘数据的高阶统计特性,是目前较常使用的特征提取方法.本文提出了一种基于优选样本的核主成份分析高光谱图像降维方法,算法挑选参与核主成份分析运算的样本时兼顾整幅高光谱图像的统计特性,以与全图能量分布相近的最小样本集为最终选择样本.本算法由IDL7.0实现,并在实际高光谱图像Cuprite上进行实验.结果表明,在大幅缩短运算时间的同时,降维效果优于传统的核主成份分析方法.     

蒙特卡罗临界计算全局计数效率新算法研究

为提高蒙特卡罗临界计算时全局计数的整体效率, 对比分析了新提出的均匀计数密度算法、均匀径迹数密度算法和原有的均匀裂变点算法. 以大亚湾核反应堆pin-by-pin模型的全局体平均通量计数和中子沉积能计数为例, 前两种算法较均匀裂变点算法都获得了整体效率的提高. 上述算法已经在自主开发的并行蒙特卡罗输运程序JMCT上予以实现.