基于遗传算法去除磁共振图像的运动伪影

病人在磁共振成像扫描过程中的运动会在图像中产生运动伪影,影响图像质量与临床诊断。提出了一种去除磁共振图像运动伪影的图像重建方法,通过遗传算法筛选出受运动影响较小的k空间数据,利用压缩感知进行重建。设计了一种无需参考图像的图像质量评估方法,与全参考图像质量评价指标呈强相关性。实验结果表明,算法可减轻运动伪影,提高图像质量。     

应用先验插值校正CT金属伪影

针对CT系统在实际应用中出现的金属伪影问题,提出一种基于先验插值的金属伪影校正算法。文中通过预滤波、骨骼分割和软组织恢复步骤计算先验图像,并利用先验图像的正向投影对原始投影中的金属投影区进行插值校正。应用该算法对数值仿真图像和临床CT图像分别进行了校正重建实验。数值仿真实验表明,用提出算法校正的结果比线性插值金属伪影校正算法、归一化金属伪影校正算法校正的结果更接近理想体模。临床数据实验表明:该算法的重建结果有效抑制了金属伪影,清晰重建出金属边缘细节,极大地提高了重建图像的质量。     

磁共振成像PROPELLER数据网格化中的采样密度补偿新算法

PROPELLER磁共振数据的重建是一个典型的K空间非笛卡尔采样数据的重建问题.由于现有网格化重建算法中的密度补偿需要计算每个采样点的密度补偿系数,须对非笛卡尔分布的数据进行卷积运算,给定N采样点,该卷积运算需要N×N/2次距离运算,由于PROPELLER采集的数据量N很大,计算耗时非常长.本文提出PROPELLER数据网格化重建中的密度补偿新算法,通过基于网格化分量全为1的向量来计算在均匀网格点上的采样密度分布值进而加以补偿,使得算法复杂度大大下降.实验表明,本文算法比现有算法的运行时间缩短400多倍,而重建质量与原有算法基本相同.     

一种并行磁共振成像伪影消除方法

多线圈采集技术与并行成像算法通过降低磁共振成像所必需的梯度编码步数有效提高了成像的扫描速度.但是在数据采集过程中,运动常常会使线圈数据发生异常,从而对最终重建图像质量产生很大影响.本文提出了一种新的重建算法去消除重建图像中产生的伪影.算法把破坏数据看成观测数据样本中的异常值,应用了AM鲁棒估计进行数据修正,很好的抑制了异常值对数据集造成的影响.本研究分别对多线圈并行采集的体模数据与真实脑部数据进行了实验,结果显示算法可以有效消除破坏数据在重建图像中产生的伪影,并通过对比实验充分显示了本算法的优越性.     

一种改进的EPI伪影校正算法

EPI是功能磁共振成像(fMRI)广泛采用的一种扫描技术。成像物体的磁敏感性、化学位移、静磁场不均匀性以及系统硬件性能的不完善等因素导致EPI图像在相位编码方向存明显的ghost伪影,严重破坏了图像的质量。研究基于图像的后处理方法校正EPI伪影具有明显的实际临床应用价值。首先分析了现行相位校正算法和相位恢复算法的不足之处,然后提出了一种改进的EPI伪影校正算法。改进的算法充分利用两种方法的优点,并克服了各自的缺点,较好地实现了伪影校正。通过对实际的phantom图像处理,阐明了改进算法的稳定性及可靠性。     

一种局部CT图像重建算法研究

CT作为一种无创检查设备在医学领域得到广泛的应用,但是受重建方法的限制,它通常只能对相对静止的器官进行扫描断层成像,而对运动物体或器官的成像能力较差,通常会存在运动伪影.克服运动伪影的根本途径就是减少数据采集的时间,这可以通过减少采集范围来实现.中间函数重建算法是一种基于傅里叶空间的重建算法,该算法可以选择感兴趣的部位(ROI)进行局部图像重建,减少数据采集的时间,因此可以使用该算法改善临床上运动物体的扫描成像.     

基于AR模型的最大熵磁共振成像重建算法

本文提出了一种基于ＡＲ模型的磁共振成像算法，通过所获得的自回归系数及线性预测误差，替代了传统的快速傅里叶（ＦＦＴ）重建方法，获得了满意的重建图像，由于在实际系统中，只能得到有限的频谱数据，利用传统的ＦＦＴ方法重建磁共振图像，将导致截断伪影和低的分辨率。本算法利用ＡＲ模型外推未知频谱数据，替代ＦＦＴ方法的填零法重建，并利用ＢＵＲＧ算法中的ＡＲ模型参数计算的有效性，不仅消除了截断伪影，抑制噪声提高了分     

融合局部低秩先验与Bloch流形约束的磁共振指纹重建算法

为了实现快速成像，磁共振指纹（Magnetic Resonance Fingerprinting,MRF）技术通常使用非笛卡尔稀疏采样模板对K空间进行高度欠采样，从而获得稀疏K空间信号.然而，从稀疏的K空间信号重建像空间数据是一个病态不适定问题，重建出的MRF像空间数据存在大量的混叠伪影，直接影响到组织生理参数的重建准确度.为此需要将各种先验知识引入重建模型之中，以缓解MRF重建问题的不适定性.针对上述问题，本文提出一种融合局部低秩先验与Bloch流形约束的MRF重建模型，并使用交替方向乘子法（Alternating Direction Method of Multipliers,ADMM）求解模型中的非凸MRF重建问题.本文算法在引入MRF像空间数据的局部低秩先验的同时，使用预先构建的字典为重建指纹提供流形约束.一方面通过空域局部低秩约束有效抑制混叠伪影的产生，另一方面利用字典先验避免指纹的时域流形特征在迭代重建过程中丢失.仿真实验结果表明，相较于引入了全局低秩先验与Bloch流形约束的其他同类算法，本文算法可以提供更高的组织参数重建准确度.     

采用高频能量的CT几何参数自标定方法

针对锥束CT系统中几何参数失配引起几何伪影的问题,提出了一种采用空域高频能量的几何伪影自校正算法。该算法以重建图像的空域高频能量为目标函数建立优化模型,通过单纯形法迭代求解使空域高频能量最大的几何参数最优解。利用投影图像的特性提取部分参数作为输入初值,减小算法搜索范围。并采用图形处理器（Graphic Processing Unit,GPU）对自校正过程中的图像重建并行加速,减少重建时间,提高校正速度。实验结果表明:该算法具有较高的求解精度,最大相对误差不超过5%,对重建图像中的几何伪影有较好的校正效果。同时,在不影响精度的情况下减少了迭代次数,算法执行效率提高了18.8%。     

基于补偿特征图滤波算法去除光学相干层析造影成像俯视图的运动伪影

运动伪影是光学相干层析造影成像(OCTA)俯视图中的一个重要问题.提出一种补偿特征图像滤波(CEF)算法去除光学相干层析造影成像俯视图的运动伪影.首先,利用奇异值分解(SVD)将光学相干层析造影成像俯视图展开为一系列的特征图像;然后,利用高通特征图像和从第一特征图像中抽取得到的正交补偿积这两部分来重建恢复图像.实验结果表明,CEF算法能较好地去除条纹噪声,经CEF算法复原得到的图像有着较传统特征图滤波算法更好的图像质量.该算法为其他扫描成像系统去除类似的条纹噪声提供了新的参考.     

Motion compensation using backward prediction and prediction refinement

This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion estimation is based on previously decoded field-images. The motion is then temporally predicted and used for motion compensated prediction of the field-image to be coded. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.     

Backward motion compensation for interlaced HDTV

The pel-recursive approach to motion estimation has been widely studied for compensating progressively scanned, moderate-resolution video. Although pel-recursive algorithms may not be suitable for application to interlaced high-definition television (HDTV), the underlying principle of backward motion compensation, upon which pel-recursive algorithms are based, can be exploited to improve the existing motion compensation algorithms. This paper proposes applying a backward approach to motion compensation to improve the performance of standard block-based algorithms for motion-compensated interlaced HDTV sequences. First, we describe a framework for motion compensation in which motion information is parameterized by a motion operator and a domain for that operator. Within this framework, we characterize the type of motion information represented by forward (e.g. block-based) and backward approaches to motion compensation. We propose a method for combining these two sources of motion information to form an optimal motion-compensated prediction. Simulations on two interlaced HDTV sequences demonstrate performance improvements between 1 and 2 dB over standard block-based methods.     

基于Kalman预测的自适应De-interlace算法

本文提出一种基于Kalman预测的自适应De-interlace算法.它包括运动块检测、自适应的运动估计以及对运动块的运动补偿和对静止块的场复制等模块.其中,运动块检测用了连续的4场图像,能够更精确地实现运动检测;对检测到的运动块用Kalman滤波进行自适应地运动估计,并根据估计到的运动矢量进行运动补偿;对静止块,用场复制法进行De-interlace处理.该算法在de-interlace性能接近全搜索算法的前提下,运算量降低了三个数量级.     

Recognition of dynamic video contents with global probabilistic models of visual motion.

The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos.     

A spatial hybrid motion compliant mechanism: Design and optimization

A hybrid motion system is defined as a mechanical system that combines a macro motion and a micro motion into one system to achieve a large motion and high resolution with fast response simultaneously. In this paper, a spatial hybrid motion mechanism with 3-DOFs is developed that integrates two types of motion through only one compliant mechanism: a macro motion driven by DC servomotors for large workspace and a micro motion driven by PZT actuators for high precision. A unique feature of the developed hybrid motion compliant mechanism is the elimination of coupling interaction between the macro motion and the micro motion by properly structure design. Three issues are addressed in this paper: (1) design principle and implementation of the hybrid motion mechanism; (2) kinematic analysis and dynamic analysis; and (3) optimization design of the hybrid motion mechanism. A spatial hybrid motion mechanism is developed and the optimization is conducted. The Taguchi method is used to identify significant parameters in the design optimization, and finite element analysis results verify the design principle of the parallel architecture for the hybrid motion mechanism.     

面向目标检测的多尺度运动注意力融合算法研究

运动目标检测是视频分析领域的关键技术之一,针对目前全局运动场景下目标检测算法的局限性,该文提出一种多尺度运动注意力融合的目标检测算法,为目标检测问题提供了新思路。该算法通过时-空滤波去除运动矢量场噪声,根据运动注意力形成机理定义运动注意力模型;为提高注意力计算的准确性,定义了目标像素块的测度公式,采用D-S证据理论对多尺度空间运动注意力进行决策融合,最终获取运动目标区域位置。多个不同高清视频序列的测试结果表明,该文算法在全局运动场景中能准确对目标进行检测定位,从而有效克服了现有算法的局限性。     

基于头肩图像中脸部轮廓的运动估值

对于可视电话中的头肩图像,在提取出脸部轮廓后,在脸部区通过搜索出全局的二维运动参数来确定各像块运动矢量的初始搜索点。在此基础上搜索精确的运动矢量时,搜索范围很小,搜索结果更符合真实运动,保证了运动矢量场的一致性。因此,既加快了运动估值的速度,又节省了编码运动矢量所用的比特数,使图像的质量有所提高。     

Automatic correction of motion artifacts in magnetic resonanceimages using an entropy focus criterion

Presents the use of an entropy focus criterion to enable automatic focusing of motion corrupted magnetic resonance images. The authors demonstrate the principle using illustrative examples from cooperative volunteers. Their technique can determine unknown patient motion or use knowledge of motion from other measures as a starting estimate. The motion estimate is used to compensate the acquired data and is iteratively refined using the image entropy. Entropy focuses the whole image principally by favoring the removal of motion induced ghosts and blurring from otherwise dark regions of the image. Using only the image data, and no special hardware or pulse sequences, the authors demonstrate correction for arbitrary rigid-body translational motion in the imaging plane and for a single rotation. Extension to three-dimensional (3-D) and more general motion should be possible. The algorithm is able to determine volunteer motion well. The mean absolute deviation between algorithm and navigator-echo-determined motion is comparable to the displacement step size used in the algorithm. Local deviations from the recorded motion or navigator-determined motion are explained and the authors indicate how enhanced focus criteria may be derived. In all cases they were able to compensate images for patient motion, reducing blurring and ghosting     

Optical Flow Estimation for a Periodic Image Sequence

We propose a temporal modeling approach for determining image motion from a sequence of images wherein the inherent motion is periodic over time. To exploit the periodic nature of the motion, we use a Fourier harmonic representation to model the temporal evolution of the motion field for the entire sequence. We then determine the motion field simultaneously for the different image frames by estimating the parameters of this representation model, where the model order in the Fourier representation serves as a regularization parameter on the temporal coherence of the motion field. This approach can take advantage of the statistics of all the available data in the image sequence. In our experiments, we tested the proposed approach on several motion types at different noise levels, including translational motion, convergent/divergent motion, and cardiac motion. Our results demonstrate that this approach could lead to more robust estimation of the motion field in the presence of strong imaging noise compared to a frame-by-frame estimation approach.     

Quantifying motion in video recordings of neonatal seizures by robust motion trackers based on block motion models

This paper introduces a methodology for the development of robust motion trackers for video based on block motion models. According to this methodology, the motion of a site between two successive frames is estimated by minimizing an error function defined in terms of the intensities at these frames. The proposed methodology is used to develop robust motion trackers that rely on fractional block motion models. The motion trackers developed in this paper are utilized to extract motor activity signals from video recordings of neonatal seizures. The experimental results reveal that the proposed motion trackers are more accurate and reliable than existing motion tracking methods relying on pure translation and affine block motion models.