利用CAD模型的不完全扫描CT图像重建

工业计算机断层成像(CT)扫描大尺寸和高密度工件时,会出现穿不透、扫描角度有限导致的投影数据不完备、重建伪影严重等问题。基于此,提出一种将工件的CAD设计模型作为先验知识,来实现该类对象的有限角CT重建优质图像的方法。由工件CT扫描的断层位置计算CAD模型对应的分层位置,并得到模型的像素截面;根据CT系统X射线能量和模型中各部分材质,确定和生成一幅衰减系数图像,并将其配准到一幅零灰度图像中,得到先验图像;最后对扫描投影数据进行SART+TVM+PRIOR算法重建,得到重建图像。仿真实验和实际工件扫描实验的结果显示,加入先验图像后重建的图像质量要远远优于未加入先验图像的重建结果,边缘结构更加清晰,并极大地减少了伪影。     

硬化伪影的新型表现形式及其校正

X射线硬化是导致工业CT重建图像质量下降的物理原因之一。硬化伪影通常表现为两种形式,即杯状伪影和带状伪影。描述并证实了硬化伪影的一种新型表现形式,这种伪影与真实结构相关,且分布规则,容易造成伪影与真实结构的混淆。采用线性化校正方法对该伪影进行抑制,提高了重建图像质量,改善了通过CT重建图像进行几何测量的精度。     

Micro CT中混叠伪影的消除

Micro CT系统是近几年发展起来的具有极高空间分辨率和探测灵敏度的一种成像技术,专门用于对小动物进行成像,并被广泛应用于药物开发,肿瘤检测.Micro CT的这种高分辨率使存在于CT中的混叠伪影在Micro CT中更为突出,成为影响图像质量,降低图像分辨率的主要因素.本文模拟分析了伪影产生的机理,并提出了一种直接对重构的切片图进行伪影分析及消除的算法,能快速有效的消除Micro CT切片图像里的混叠伪影,伪影消除率达70%以上,并且使图像的对比度提高. 关键词： Micro CT 混叠伪影 自适应滤波     

工业CT图像均匀性校正

在工业CT图像重建过程中,射束硬化使得图像出现“杯状”伪影,阵列探测器响应不一致使得图像出现环状伪影、带状伪影和点伪影,影响图像均匀性.本文分析了射束硬化的原理和探测器响应不一致的数学模型,并通过实验,对探测器响应进行多挡板定标得到校正系数表.利用查表插值的方法对投影数据进行均匀性校正,消除上述伪影.     

X射线CT正交基材料分解成像方法及其在校正金属伪影中的应用

针对被测样品的组成物质已知且彼此不混合的情况,提出了一种结合能谱信息的单能谱计算机断层扫描（CT）图像重建方法。该方法利用已知物质作为基材料对CT投影数据的采集过程进行数学建模,然后对该非线性模型进行基材料图像的迭代求解。在求解中,通过将基材料“不混合”的性质转化为向量正交性,实现了迭代过程的快速收敛。本文方法充分考虑了X射线的能谱和被测样品材料的属性,可显著地校正传统CT图像中的硬化伪影和金属伪影,有效地提高该类样品的CT成像质量。实验验证了所提方法的有效性。     

基于投影融合的多层球壳双能CT算法研究

对高密度差多层球壳结构件进行双能CT检测，需要研究同时基于高低能射线两套投影数据的图像重建方法。先分别重建再进行图像融合的方法，不能充分利用两种能量射线投影的互补信息，重建图像质量不高。针对高密度差多层球壳结构特征，通过设计限定投影扫描方式获取区域分布明确的高、低能射线投影正弦图，基于正弦图区域分割重组和交集数据等值化一致性处理实现双能投影数据融合，在此基础上完成图像重建。根据以上设计归纳出基于投影融合的多层球壳双能CT重建算法，仿真结果表明算法可行，且重建结果明显好于基于图像融合的方法。     

基于CT扫描数据的X射线能谱估计方法

X射线能谱在计算机层析成像(computed tomography,CT)图像硬化校正、双能谱CT成像、辐射剂量计算等方面具有重要作用.常用的估计X射线能谱分布的方法,利用X射线穿过不同厚度模体的衰减数据,来间接估计X射线能谱分布.由于该问题具有严重的病态性,因此如何鲁棒和准确地求解是能谱估计问题的关键.本文提出了一种利用CT扫描数据来估计X射线能谱分布的方法.该方法中考虑了谱估计和图像重建之间的相互印证关系,即谱估计正确时重建的CT图像无硬化伪影,而重建图像无硬化伪影时,则说明估计的谱准确.该方法利用这种相互印证关系构造优化模型,通过交替迭代求解,估计能谱分布和重建无硬化伪影的CT图像.数值实验和实际实验结果表明,该方法可以准确、鲁棒地估计出X射线能谱.     

RT二维CT扫描的精确重建

基于多个旋转中心的旋转/平移扫描方法(RT扫描)可利用近轴X射线对大型回转试件进行二维CT检测。同传统的二代CT平移/旋转扫描(TR)相比,RT二维CT扫描的扫描时间是TR二维CT扫描时间的1/3。但由RT二维CT扫描插值全局重建的图像,在物体内部,其特征边缘有明显的伪影。采用一种精确的分区域重建方法可消除这种伪影。计算机仿真验证了这种重建算法的可行性。     

X射线CT环形伪影去除方法

为去除X射线CT图像中的环形伪影,建立了一种投影数据预处理方法。该方法通过对各角度投影数据逐一进行分段多项式拟合的方式建立异常探元的备选校正因子集合,再根据备选校正因子的概率密度分布确定最可几校正因子。介绍了方法的物理依据、原理及其实现步骤,并分析了其在复杂条件下的适应性。结果表明:该方法对稀疏环形伪影、密集环形伪影以及伴随强噪声污染的CT图像环形伪影均可去除;与中值滤波等方法相比,可以更好地保持图像的空间分辨率。该方法可用于多材质检测对象CT图像的处理。     

两种CT成像环状伪影校正方法

探测器探元响应的不一致性导致计算机层析成像(CT)图像产生环状伪影,使得像质下降,影响了缺陷的判读以及图像的后续处理与量化分析.提出了两种CT成像环状伪影校正方法:基于特征识别的校正方法和基于B样条曲线拟合的校正方法.前者针对单个独立探元或者少数几个相邻探元存在突出响应不一致的情况.该方法首先识别出响应不一致探元的位置,然后在该位置执行局部中值滤波以消除正弦图中的竖直条纹.后者针对多个连续探元同时存在微小响应不一致的情况.该方法用B样条曲线拟合的方法确定各个探元的归一化增益系数,进而对探元的响应不一致性进行校正.两种方法各有特点,互为补充,共同组成较为完备的环状伪影校正体系.实验结果验证了两种方法的有效性.     

Joint Reconstruction of Multi-contrast Images and Multi-channel Coil Sensitivities

Magnetic resonance imaging (MRI) has an important feature that it provides multiple images with different contrasts for complementary diagnostic information. However, a large amount of data is needed for multi-contrast images depiction, and thus, the scan is time-consuming. Many methods based on parallel magnetic resonance imaging (pMRI) and compressed sensing (CS) are applied to accelerate multi-contrast MR imaging. Nevertheless, the image reconstructed by sophisticated pMRI methods contains residual aliasing artifact that degrades the quality of the image when the acceleration factor is high. Other methods based on CS always suffer the regularization parameter-selecting problem. To address these issues, a new method is presented for joint multi-contrast image reconstruction and coil sensitivity estimation. The coil sensitivities can be shared during the reconstruction due to the identity of coil sensitivity profiles of different contrast images for imaging stationary tissues. The proposed method uses the coil sensitivities as sharable information during the reconstruction to improve the reconstruction quality. As a result, the residual aliasing artifact can be effectively removed in the reconstructed multi-contrast images even if the acceleration factor is high. Besides, as there is no regularization term in the proposed method, the troublesome regularization parameter selection in the CS can also be avoided. Results from multi-contrast in vivo experiments demonstrated that multi-contrast images can be jointly reconstructed by the proposed method with effective removal of the residual aliasing artifact at a high acceleration factor.     

高密度差多层球壳双能CT图像重建方法

对含高密度差材料的大尺寸多层球壳构件采用双能CT方法进行无损检测,当采用高能射线扫描时,低Z材料区对射线基本无阻挡,而采用低能射线扫描时又会被高Z材料区截断,因此需要根据双能射线投影特征开展针对性的图像重建方法研究。首先,根据材料结构特征建立仿真模型,分别模拟得到高、低能射线的投影正弦图,然后采用滤波反投影和代数重建法等算法进行图像重建。通过仿真和比较分析,得出先融合投影再重建图像的方法要整体优于先分别重建再融合图像的方法。另外,为保证图像重建质量,在进行扫描射线能量选择时,要考虑提高局部投影质量,更要尽量减少被截断的总投影量。     

基于能谱CT的材料组分彩色表征研究

基于光子计数探测器的X射线能谱CT,通过增加能谱分辨率实现了CT图像由灰度向彩色的转变,提高了材料识别能力。然而随着能谱通道数量的增加,通道中的噪声也显著增加,降低了材料识别的准确性。为充分利用能谱CT图像的稀疏性以及能谱通道之间图像的相关性,提出一种多约束窄谱CT迭代重建算法,可在降低噪声的同时有效保留图像的边缘及细节部分。进一步利用主成分分析对窄谱CT图像中的能谱信息进行估计,并建立主成分图像与R,G和B颜色分量之间的映射关系,最后获取彩色CT图像。该方法通过材料组分的彩色表征可以有效识别材料,同时降低图像中的背景噪声。仿真实验和实际实验结果验证了多约束窄谱CT迭代重建算法的有效性以及利用主成分分析进行材料组分彩色表征的可行性。     

Dictionary learning based sinogram inpainting for CT sparse reconstruction

In CT (computed tomography), reconstruction from undersampling projection data is often ill-posed and suffers from severe artifact in the reconstructed images. To overcome this problem, this paper proposes a sinogram inpainting method based on recently rising sparse representation technology. In this approach, a dictionary learning based inpainting is used to estimate the missing projection data. The final image can be reconstructed by the analytic filtered back projection (FBP) reconstruction. We conduct experiments using both simulated and real phantom data. Compared to the comparative interpolation method, visual and numerical results validate the clinical potential of the proposed method.     

4-D image reconstruction for Streak Tube Imaging Lidar

The Streak Tube Imaging Lidar (STIL) which is the flash laser radar is developed in recently years. It can output the 4-D image (3-D range image + 1-D intensity image). The core part of STIL is the streak tube, which is a line array detector. STIL can directly collect the image of returned light pulse, and consequently, it can’t output the 4-D target image. In the paper, the peak detection as the reconstruction algorithm is used to reconstruct the multiple streak tube images, and the clear 4-D target images are obtained. The outdoor imaging experiments are completed for the far distance buildings. The reconstruction for the multiple streak tube images is finished, and the results state that the peak detection algorithm can output the high-resolution 4-D target images. Original Text ? Astro, Ltd., 2009. The article is published in the original.     

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.     

A modified equally sloped algorithm based on the total variation algorithm in computed tomography for insufficient data

Computed tomography (CT) has become an important technique for analyzing the inner structures of material, biological and energy fields. However, there are often challenges in the practical application of CT due to insufficient data. For example, the maximum rotation angle of the sample stage is limited by sample space or image reconstruction from the limited number of views required to reduce the X‐ray dose delivered to the sample. Therefore, it is difficult to acquire CT images with complete data. In this work, an iterative reconstruction algorithm based on the minimization of the image total variation (TV) has been utilized to develop equally sloped tomography (EST), and the reconstruction was carried out from limited‐angle, few‐view and noisy data. A synchrotron CT experiment on hydroxyapatite was also carried out to demonstrate the ability of the TV‐EST algorithm. The results indicated that the new TV‐EST algorithm was capable of achieving high‐quality reconstructions from projections with insufficient data.     

Parallel variable-density spiral imaging using nonlocal total variation reconstruction

The relatively long scan time is still a bottleneck for both clinical applications and research of magnetic resonance imaging. To reduce the data acquisition time, we propose a novel fast magnetic resonance imaging method based on parallel variable-density spiral acquisition, which combines undersampling optimization and nonlocal total variation reconstruction.The undersampling optimization promotes the incoherence of resultant aliasing artifact via the "worst-case" residual error metric, and thus accelerates the data acquisition. Moreover, nonlocal total variation reconstruction is utilized to remove such an incoherent aliasing artifact and so improve image quality. The feasibility of the proposed method is demonstrated by both numerical phantom simulation and in vivo experiment. The experimental results show that the proposed method can achieve high acceleration factor and effectively remove an aliasing artifact from data undersampling with well-preserved image details. The image quality is better than that achieved with the total variation method.