高压电缆源平移扫描CT高效重建算法EI北大核心CSCD

为提高在役高压电缆X射线扫描图像重建速度,结合传统的滤波反投影算法,提出基于数据平滑的局部扫描重建算法。分析了源直线扫描局部计算机断层成像方法存在的有限角和数据截断问题,提出数据平滑方式解决方法。主要途径是利用余弦函数沿探测器方向和射线源方向分别进行数据平滑插值,避免投影数据值在这两个方向上突然降为零,起到抑制截断伪影和有限角伪影的作用。实验证明,所提方法能有效抑制图像伪影,有利于电缆阻水缓冲层缺陷识别。相较于原有的联合迭代重建算法,该方法缩短了重建时间且图像重建质量相当;相较于传统滤波反投影算法,重建图像质量提高但重建耗时基本一致。     

基于差分曲率的各项异性扩散小波图像降噪算法研究

在$\gamma $放射性内污染修正均匀冗余阵列编码成像中,为了进一步减少使用最大似然估计(Maximum Likelihood Expectation Maximisation,MLEM)算法进行编码图像重建的伪影,并且提高重建的效率,本文提出了基于差分曲率的各项异性扩散小波图像降噪(Anisotropic Diffusion Wavelet Image Denoising based on Differential Curvature, 简称ADWIDDC)算法, 结合MLEM算法得到了改进的应用于编码成像的MLEM-ADWIDDC算法。该算法首先采用MLEM算法对探测器得到的投影数据进行图像重建,再利用互补成像方法消减近场伪影,最后采用ADWIDDC算法进一步降低图像伪影。模拟结果表明,对于131I源呈圆环分布的编码图像,以重建算法运行时间为195 s为截止条件,MLEM-ADWIDDC算法能够更好地去除伪影,重建的$\gamma $放射源图像纹理也更加清晰;对于131I源呈“CDUT”分布的编码图像,以重建图像信噪比达到5.10 dB为截止条件,本文的MLEM-ADWIDDC算法运行时间为98.36 s,比仅采用MLEM算法消耗的时间缩短了49%。     

改进的大锥角锥束CT圆轨迹反投影滤波算法北大核心CSCD

锥束CT圆轨迹反投影滤波(BPF)算法能够有效处理数据截断问题,在感兴趣区域成像研究中具有重要的地位。但是当锥角增大时,重建图像的顶端和底端会出现严重的伪影,从而影响图像质量。为解决该问题,从扇束圆轨迹BPF算法出发,重新推导了锥束圆轨迹BPF算法,通过对锥束伪影成因分析,提出了一种基于锥角大小和轴向距离大小的权重函数,修正了重建过程中差分反投影图像求解部分,达到抑制伪影的目的。实验结果表明,改进的大锥角锥束CT圆轨迹BPF算法能够在锥角增大时有效地抑制锥角效应,提高重建图像质量。     

CS-MRI 中评价随机欠采样矩阵的新方法

在压缩感知-磁共振成像(CS-MRI)中,随机欠采样矩阵与重建图像质量密切相关．而选取随机欠采样矩阵一般是通过计算点扩散函数(PSF),以可能产生的伪影的最大值为评价参数,评估欠采样对图像重建的影响,然而最大值只反应了伪影的最坏情况．该文引入了两种新的统计学评价参数平均值(MV)和标准差(SD),其中平均值评估了伪影的平均大小,标准差可以反映伪影的波动情况．该文分别使用这3种参数对小鼠和人体脑部MRI数据以不同的采样比率进行CS图像重建,实验结果表明,当采样比率不低于4倍稀疏度时,使用平均值获得了质量更优的重建图像．因此,通过稀疏度先验知识指导合理选取采样比率,并以平均值为评价参数选取随机欠采样矩阵,能够获得更优的CS-MRI重建图像．
     

基于图像总变分和张量字典的多能谱CT材料识别研究

基于光子计数探测器的多能谱计算机断层成像技术(CT),能够获得多个能量段的能谱信息,在材料识别方面有着独特的优势。由于窄能谱探测及光子计数探测器存在一致性差的问题,多能谱CT图像中含有较多的噪声和伪影,这不利于材料的分解与识别。因此从重建的角度出发,改进了传统张量字典学习(TDL)方法,提出一种基于图像总变分(TV)和TDL的图像重建算法,简称TV+TDL。该算法不但继承了TDL算法在刻画各个能量通道图像之间相似性的优势,而且通过引进TV作为正则项,可进一步恢复图像微小结构和细节并有效地抑制噪声,提高材料分解精度。仿真实验结果表明,TV+TDL算法能够有效重建高质量的多能谱CT图像,并成功实现基材料模型下的材料分解与识别,从而验证了该方法的有效性和实用性。     

基于成像差分吸收光谱技术和压缩感知理论的烟囱烟羽断层重建研究

烟羽断层重建质量受两方面条件限制：其中一个限制条件是遥感设备的时间分辨率。以往的研究多使用多轴差分吸收光谱仪（MAX-DOAS）进行CT重建,受采集数据速度的限制,重建图像的时间分辨率较低。另一个限制条件是,采集到的数据量有限,是典型的不完全角度重建。过去多使用代数迭代重建算法或统计迭代重建算法,重建图像受测量误差的影响比较大,分辨率较低且伪影较多。构造了基于成像差分吸收光谱技术（IDOAS）的光谱数据采集系统,与多轴差分吸收光谱仪构造的系统相比,数据采集的时间分辨率提高了160多倍,基本解决了时间分辨率的问题。提出了一种基于压缩感知理论和低三阶导数模型的烟羽断层重建算法--投影凸函数集低三阶导数法,简称为POCS-LTD。在投影的过程中,使用代数重建算法使重建图像符合投影方程;在全变分迭代的过程中使用了优化算法,将低三阶导数模型的全变分归一化值作为优化算法的迭代方向,前次迭代运算结果与本次投影运算的差值的模作为迭代步长。对重建算法进行了数值模拟,并以重建图像的接近度和一致性相关因子为指标,对重建结果进行了分析。数值模拟表明,算法具有良好的抗误差能力,与传统的低三阶导数法相比,本文提出的算法将重建接近度减小了80%以上。使用烟羽数据采集系统进行了外场实验,用POCS-LTD算法对外场实验的数据进行了烟羽重建,重建图像显示烟羽图像清晰,伪影得到了较好的抑制。介绍的烟羽断层数据采集系统和烟羽断层重建算法,提高了烟羽断层重建图像的时间分辨率,减少了重建图像的伪影,扩大了光谱测量技术的应用范围。     

基于z轴权重的麦粒图像三维重建

为得到清晰的麦粒重建切片,利用共轭射线弥补Radon空间数据缺失,在FDK(Feldkamp-Davis-Kress)重建算法的基础上,引入z轴权重函数来优化麦粒切片图像的重建结果。针对三维头模型,z-FDK算法重建结果的均方根误差比FDK算法的小3.6927,大大抑制了FDK算法中的z轴方向强度下降。实验结果表明,针对卵期、低龄幼虫期和高龄幼虫期的麦粒投影图像,z-FDK算法的平均梯度和对比度噪声比均大于FDK算法,重建麦粒茸毛端和胚部端两个区域的灰度值增大,伪影得到改善。     

激光反射断层成像相位恢复方法

针对激光反射断层成像过程中存在目标的平动和抖动所产生的旋转中心偏移导致重建图像错位形成伪影的问题,提出基于相位恢复技术的改进误差下降算法.该方法通过光强反复迭代得到相位分布,获得重构目标丢失的相位信息,通过增加初始约束条件以及空间域和频域的限制有效改善算法的收敛性.运用改进的误差下降算法,有效改善了相位恢复方法容易陷入局部极小解的问题,获得了优化的重构图像.仿真实验表明,三组投影数据重建图像的平均相对均方误差由0.774下降为0.551,能够有效消除图像重建伪影.外场实验结果表明此方法使激光反射断层成像系统分辨率得到有效改善.     

基于极坐标变换去除计算机层析图像环形伪影

探测器像元响应的不一致、光源能不稳定等因素使得计算机层析(CT)图像中含有较多的环形伪影,严重降低了图像质量,影响了图像的三维重建和量化分析,为此提出了基于极坐标变换与傅里叶变换后低通滤波的算法去除环形伪影。通过极坐标变换将直角坐标下的环形伪影转化为极坐标下的线性伪影,然后对线性伪影图像进行傅里叶变换获得频谱图像,进而设计二维低通滤波器进行滤波处理,最后通过傅里叶逆变换与坐标逆变换获得校正后的图像。利用Matlab软件,编写程序对算法进行验证,结果表明,该算法能够有效地去除环形伪影,使图像内部细节清晰可见,并且保护了图像边缘信息,提高了图像的信噪比;另外,使用该方法处理100张切片图像只需3.5min,可满足批量处理的需求。     

多金属物体CT图像的金属伪影校正

针对多金属伪影的校正问题,本文通过仿真实验分析了多金属伪影的成因,并提出了一种基于投影校正的多金属伪影校正方法.该方法首先直接从投影域分割出金属区域,然后建立对金属区域投影值的校正模型,最后通过调整模型参数达到校正目的.模型以重建图像的灰度熵为目标函数,采用单纯形法迭代求解使熵最小时的校正参数.仿真和实际数据的实验结果表明,本文算法对多金属伪影的校正起到了良好的效果,且校正后的图像质量优于插值校正法.     

复合扫描全息术及透过强散射介质三维成像研究

应用菲涅耳波带片光学扫描全息术原理,研究强散射介质中吸收体的三维成像.针对由成像物体强散射背景产生的背景噪音,提出复合扫描全息术成像方法.实验中应用这一方法,对嵌埋在浓度为1%,深度为1.7 cm的脂肪乳剂中的吸收体成像,得到了信噪比和对比度较好的再现像.在此基础上,对复合扫描全息成像系统的信噪比,对比度及分辨率等性能进行了深入的理论分析和实验测试.研究表明,与单一模式相比,复合扫描全息术在信噪比和对比度方面有较大改善,但分辨率还需进一步提高.     

低能X射线工业CT图像杯状伪影校正

为了去除X射线工业CT图像中的杯状伪影,提高CT图像的识别能力和量化分析精度,提出一种基于分度投影和权函数的射束硬化校正方法。首先分析得出杯状伪影主要是由X射线连续谱穿过被测物体过程中出现的射束硬化所导致。然后扫描阶梯模型,采集不同厚度下的投影数据并求出线衰减系数,通过拟合曲线,得到硬化模型函数和权函数校正模型函数,并确定权函数。接着,扫描被测圆柱形工件,采集不同分度下的投影数据。最后,针对每一个分度投影数据,采用权函数与当前分度投影数据乘积的方法进行硬化校正。对含有杯状伪影的实际CT图像进行了校正实验,结果表明,与多项式拟合法相比,该方法校正后的灰度图像没有放大噪声,且信噪比提高3.29%,有效地消除了杯状伪影,同时较好地保留了图像边界细节。     

Scatter correction for cone-beam computed tomography using self-adaptive scatter kernel superposition

The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.     

Scatter correction for cone-beam computedtomography using self-adaptive scatterkernel superposition

The authors propose a combined scatter reduction and correction method to improve image quality in cone beam computed tomography (CBCT). The scatter kernel superposition (SKS) method has been used occasionally in previous studies. However, this method differs in that a scatter detecting blocker (SDB) was used between the X-ray source and the tested object to model the self-adaptive scatter kernel. This study first evaluates the scatter kernel parameters using the SDB, and then isolates the scatter distribution based on the SKS. The quality of image can be improved by removing the scatter distribution. The results show that the method can effectively reduce the scatter artifacts, and increase the image quality. Our approach increases the image contrast and reduces the magnitude of cupping. The accuracy of the SKS technique can be significantly improved in our method by using a self-adaptive scatter kernel. This method is computationally efficient, easy to implement, and provides scatter correction using a single scan acquisition.     

Neutron dark-field tomography

We report how a grating interferometer yields neutron dark-field scatter images for tomographic investigations. The image contrast is based on ultrasmall-angle scattering. It provides otherwise inaccessible spatially resolved information about the distribution of micrometer and submicrometer sized structural formations. Three complementary sets of tomographic data corresponding to attenuation, differential phase, and small-angle scattering can be obtained from one measurement. The method is compatible with conventional imaging and provides significantly higher efficiency than existing techniques.     

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)

Objective: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI. Methods: We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts. Results: In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast. Conclusion: Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.     

Angular scatter ultrasound imaging of wavelength scale targets

A bistatic ultrasound imaging system is demonstrated that uses two 32-element linear phased array transducers oriented at an angle of 40 degrees to one another. The system simultaneously acquires and displays in real time one conventional backscatter image and one "angular scatter" image formed using side-scattered echoes from the same B-mode sector region. Experiments are presented that show differences in the magnitudes of backscatter and angular scatter signals acquired from three nylon monofilaments with diameters less than one wavelength and from soft tissue structures in vivo. The relative magnitudes of angular scatter signals from the monofilaments are qualitatively consistent with a theoretical analysis of acoustic scattering from elastic cylinders. Larger tissue features are more clearly defined in angular scatter images. This result is attributed to the orientation of specularly reflecting surfaces and the expected influence of scattering angle on the system's sensitivity to different scatterer spacings.     

工业CT图像边缘伪影校正

为了去除工业CT图像中的边缘伪影,提高CT图像的识别能力和尺寸测量精度,提出一种降低串扰的系数修正法。首先分析得出边缘伪影主要是由散射射线在相邻探测通道之间的串扰所导致,并给出了探测通道串扰的数学模型;然后根据数学模型设计实验方案,通过对影响串扰率的主要因素进行实验分析,得到串扰率随入射X射线能量和被测物体厚度变化关系,再通过最小二乘拟合得出投影数据关于串扰率的函数;最后利用此函数对投影数据进行校正,降低了串扰的影响。实验结果表明,探测器间一级串扰率约为9.0%,二级串扰率约为1.2%,其中一级串扰是造成边缘伪影的主要因素,采用本文方法能够有效地抑制边缘伪影,同时较好地保留了图像细节和边缘。     

基于自适应正则化的超分辨率重建方法

传统正则化超分辨重建得到的图像往往存在过度平滑或伪信息残留的问题,结合超分辨重建模型对重建图像伪信息的产生进行了分析,针对传统方法的不足提出了基于图像区域信息自适应的正则化方法,通过图像的区域信息将图像划分为平滑区与非平滑区域,对不同区域选用不同的先验模型进行约束。同时考虑人眼的视觉感知特性,结合区域信息实现正则化参数的自适应选取。实验结果表明该方法在抑制重建图像伪信息的同时能有效保护细节,效果要优于传统方法与单一的先验模型约束,对于红外与可见光图像重建效果的提升提供了一定的理论参考。