运用兆伏锥形束计算机断层成像校正千伏计算机断层成像中假牙的金属伪影

本文提出一种运用兆伏锥形束CT(MVCBCT)校正千伏CT(kVCT)图像中假牙金属伪影的新方法。该方法分别用kVCT和MVCBCT扫描佩戴假牙的患者,得到两种CT图像。在kVCT中阈值分割得到金属图像,运用MVCBCT和kVCT融合得到先验图像,对先验图像前投影来替代原始金属区投影,最后通过滤波反投影(FBP)重建图像。将本文方法校正后效果与归一化金属伪影校正法(NMAR)、以MVCBCT为先验图像的归一化金属伪影校正法(NMAR-MV),以及线性插值法(LIMAR)这三种常用伪影校正方法进行比较,计算其归一化均方根偏差(NRMSD)和平均绝对偏差(MAD)。实验结果显示本文方法去除了严重的金属伪影且没有引入其他伪影,基于参考图像计算的NRMSD值和MAD值最小。NMAR、NMAR-MV、LIMAR以及本文方法的NRMSD值分别为21.0%、22.1%、41.9%、17.0%;MAD值分别为232、235、553、205 HU。本文提出的伪影校正方法能较好地去除假牙的金属伪影,大幅改善CT图像质量。     

基于图像域的Ghost伪影自动相位校正

回波平面成像过程中会产生Ghost伪影.抑制Ghost伪影的常用方法是利用参考扫描对实际扫描图像进行校正.本研究提出了一种无须参考扫描且基于图像域的自动相位校正方法.这种方法是对含伪影的图像作二维傅里叶变换,利用变换后的奇偶行数据分别重建图像以求取成像过程中奇偶回波之间的相位偏移.利用线性拟合或Marquardt-Levenber非线性拟合得到的无卷褶相位值对伪影图像进行校正,能有效抑制Ghost伪影.     

基于模糊均值聚类算法的灰度不均匀脑MR图像的分割

磁共振图像经常被含有缓慢变化的灰度不均匀场所破坏,不均匀场会造成同一组织的灰度发生变化,从而影响计算机辅助诊断的准确性.传统的基于灰度信息的分割方法对具有不均匀场的磁共振图像分割效果往往并不理想.文章改进了基于灰度信息的模糊C均值(FCM)算法,将偏移场模型、代表图像空间信息的邻域控制信息和最小二乘曲面拟合方法有机结合,能同时实现图像的校正和聚类,适用于灰度不均匀脑部磁共振图像的分割,分割精度明显优于已有的基于FCM的分割方法.     

基于改进GVF-测地线模型的颈动脉斑块的分割算法

为了在超声波图像中提取颈动脉斑块边缘,我们提出了一种基于GVF-测地线模型的图像分割算法.由于超声波图像含有大量噪声,首先使用加权均值空间平滑滤波器对图像进行预处理;再人工画出初始轮廓,分别采用GVF-Snake模型、GVF-测地线模型和改进后的GVF-测地线模型对图像进行分割,比较其结果.实验结果表明,改进后的方法分割精度很高,能够在颈动脉斑块边缘提取中取得非常好的效果.     

一种快速校正CT环形伪影的方法

背景：环形伪影严重影响了CT图像质量,对图像后处理造成困难以及容易造成误诊断。目前去除环形伪影必不可少。 目的：去除CT重建图像中的环形伪影,提高CT图像质量以及后续处理和量化分析的精度,便于诊断。 方法：首先把含环形伪影的CT图像进行线性变换,将灰度图像转换成浮点类型的图像。接着由直角坐标变换到极坐标,这样原来的环形伪影就被变换成线形伪影。设计多维滤波器,计算每个象素滤波后均值以及方差,通过与阈值比较确定伪影范围。最后通过对伪影范围进行修正以及对图像进行坐标变换,变为灰度图像。 结果与结论：通过Matlab 7.0软件设计程序,处理含环形伪影的CT图像。实验表明,此方法能有效快速地校正CT环形伪影,是一种属于图像后处理的校正方法。     

基于K空间对称性的Ghost伪影消除方法

Ghost伪影是回波平面成像（EPI）中一个很普遍的伪影，一般的方法是在扫描实际图像前利用参考扫描的方法对伪影进行消除。本研究提出了一个利用K空间原始数据的共轭对称性来消除ghost伪影的方法，且可以自动进行而不需要参考扫描。理想的K空间数据是共轭对称的。首先利用K空间的上述特性估计产生ghost伪影的K空间数据奇数和偶数行间的相位差，然后用迭代的方法对K空间数据进行校正，直到达到最好的ghost伪影消除效果。实验证明，所提出的方法可以有效的对ghost伪影进行消除。     

基于改进的k-均值聚类和数学形态学的彩色眼科图像病灶分割

病灶面积的定量化研究在眼科临床上具有重要的意义 ,为此首先需要把病灶从眼科图像中分割出来。已有的分割算法未充分利用眼科图像的整体信息 ,不适用于角膜病灶的分割。因此 ,本文提出了一种新算法 :首先利用改进的k 均值算法 ,对色度和亮度进行聚类 ,再通过数学形态学运算分割角膜病灶。实验结果表明 ,该算法能有效地分割出彩色眼科图像中的病灶     

智能肝肿瘤CT图像分割

提出了一种智能肝肿瘤CT图像分割的新方法.该方法将医学专家的高层知识融合到图像分割算法中,使算法具有智能性,能够更加准确、快速地实现分割.根据医学图像分割不同阶段的特点以及不同算法的适用性,结合了多尺度分水岭变换与模糊聚类方法,从总体上达到最佳效果.将图像空间信息引入传统的基于灰度的模糊C均值聚类算法中,对传统的模糊C均值聚类算法的目标函数进行修正,推导出修正后算法的迭代公式,并证明了迭代的收敛性.对实际CT肝肿瘤图像的分割实验结果验证了所提方法的有效性.     

一种磁共振脑组织及肿瘤区域的提取方法

脑成像的脑组织准确提取有助于提高研究脑部感兴趣区域的精度,脑部肿瘤图像分割的研究可用于组织三维重建、肿瘤体积计算等.本文使用磁共振图像,首先采用脑提取（brain extraction tool,BET）算法提取出脑组织区域,再利用一种信息熵自动阈值与形态学结合的方法;当提取出脑组织后,利用模糊C均值（fuzzy C-means,FCM）聚类算法对肿瘤区域进行提取,经过中值滤波等步骤,提取出脑肿瘤区域.与专家手工勾画对比,本文较好地提取出了脑肿瘤组织,从而实现了脑组织的自动分割,提高了工作效率.     

改进的K-均值聚类算法及其在脑组织分割中的应用

目的：鉴于K-均值聚类算法易受初始聚类中心的影响,初始聚类中心不仅影响聚类速度。还可能使算法陷入局部极小值,得到错误的聚类结果,基于SOM神经网络,提出了一种改进的K．均值聚类算法并将其应用于脑实质分割。方法：首先,由SOM神经网络对图像进行初始聚类,得到＆个聚类中心值;然后,以SOM神经网络获得的k个聚类中心值作为K_均值聚类算法的初始聚类中心对图像进行％．均值聚类,最终获得图像的聚类分割结果。结果：基于SOM神经网络的K-means聚类算法的分割精度为O．9274,K-means聚类算法的分割精度为0．8649。结论：利用改进的K-均值聚类算法对磁共振脑部图像进行了分割实验,结果表明该算法有效改善了K-means聚类算法初始聚类中心选取的盲目性,使聚类结果更为准确、稳定,取得了比单一方法更好的分割结果。     

基于卷积神经网络的宫颈CT图像的金属伪影去除

目的:为了消除宫颈CT图像中存在的金属伪影,提出一种利用卷积神经网络（CNN）去除金属伪影的策略。方法:首先通过数值仿真得到金属伪影图像与目标图像（无伪影图像）,构造训练测试数据集,利用含金属伪影的宫颈CT图像和对应的无伪影图像训练已搭建的CNN,进而得到去除宫颈CT图像金属伪影的CNN模型。结果:训练网络之前金属伪影图像与目标图像峰值信噪比（PSNR）平均值为26.098 0 dB。不同尺寸（25×25、50×50、100×100）的图像块训练网络得到去除金属伪影的图像与目标图像PSNR平均值分别为34.607 9、38.375 1、38.183 8 dB。结论:通过对仿真数据和临床数据进行实验,研究结果表明,本文方法能够快速有效地消除宫颈CT图像中的金属伪影,并且可以保留完整的组织结构信息。     

Correction of CT artifacts and its influence on Monte Carlo dose calculations

Computed tomography (CT) images of patients having metallic implants or dental fillings exhibit severe streaking artifacts. These artifacts may disallow tumor and organ delineation and compromise dose calculation outcomes in radiotherapy. We used a sinogram interpolation metal streaking artifact correction algorithm on several phantoms of exact-known compositions and on a prostate patient with two hip prostheses. We compared original CT images and artifact-corrected images of both. To evaluate the effect of the artifact correction on dose calculations, we performed Monte Carlo dose calculation in the EGSnrc/DOSXYZnrc code. For the phantoms, we performed calculations in the exact geometry, in the original CT geometry and in the artifact-corrected geometry for photon and electron beams. The maximum errors in 6 MV photon beam dose calculation were found to exceed 25% in original CT images when the standard DOSXYZnrc/CTCREATE calibration is used but less than 2% in artifact-corrected images when an extended calibration is used. The extended calibration includes an extra calibration point for a metal. The patient dose volume histograms of a hypothetical target irradiated by five 18 MV photon beams in a hypothetical treatment differ significantly in the original CT geometry and in the artifact-corrected geometry. This was found to be mostly due to miss-assignment of tissue voxels to air due to metal artifacts. We also developed a simple Monte Carlo model for a CT scanner and we simulated the contribution of scatter and beam hardening to metal streaking artifacts. We found that whereas beam hardening has a minor effect on metal artifacts, scatter is an important cause of these artifacts.     

Clinical Potential of Digital Linear Tomosynthesis Imaging of Total Joint Arthroplasty

The present study was performed to evaluate the potential for clinical application of digital linear tomosynthesis in imaging hip prostheses. Volumetric x-ray digital linear tomosysnthesis was used to image hip prostheses. The tomosynthesis was compared to metal artifact reduction (MAR) computed tomography (CT), and non-MAR CT scans of a prosthesis case. The effectiveness of this method in enhancing visibility of a prosthesis case was quantified in terms of the signal-to-noise ratio (SNR), and removal of ghosting artifacts in a prosthesis case was quantified in terms of the artifact spread function (ASF). In the near in-focus plane, the contrast is greater in the MAR CT or tomosynthesis relative to the non-MAR CT. The order of ASF performance of the algorithm was as follows: (1) tomosynthesis; (2) MAR-CT; (3) non-MAR CT. The potential usefulness of digital linear tomosynthesis for evaluation of hip prostheses was demonstrated. Further studies are required to determine the ability of digital linear tomosynthesis to quantify the spatial relationships between the metallic components of these devices as well as to identify bony changes with diagnostic consequences.     

Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering

High-density objects such as metal prostheses, surgical clips, or dental fillings generate streak-like artifacts in computed tomography images. We present a novel method for metal artifact reduction by in-painting missing information into the corrupted sinogram. The information is provided by a tissue-class model extracted from the distorted image. To this end the image is first adaptively filtered to reduce the noise content and to smooth out streak artifacts. Consecutively, the image is segmented into different material classes using a clustering algorithm. The corrupted and missing information in the original sinogram is completed using the forward projected information from the tissue-class model. The performance of the correction method is assessed on phantom images. Clinical images featuring a broad spectrum of metal artifacts are studied. Phantom and clinical studies show that metal artifacts, such as streaks, are significantly reduced and shadows in the image are eliminated. Furthermore, the novel approach improves detectability of organ contours. This can be of great relevance, for instance, in radiation therapy planning, where images affected by metal artifacts may lead to suboptimal treatment plans.     

Reduction of motion blurring artifacts using respiratory gated CT in sinogram space: a quantitative evaluation

Techniques have been developed for reducing motion blurring artifacts by using respiratory gated computed tomography (CT) in sinogram space and quantitatively evaluating the artifact reduction. A synthetic sinogram was built from multiple scans intercepting a respiratory gating window. A gated CT image was then reconstructed using the filtered back-projection algorithm. Wedge phantoms, developed for quantifying the motion artifact reduction, were scanned while being moved using a computer-controlled linear stage. The resulting artifacts appeared between the high and low density regions as an apparent feature with a Hounsfield value that was the average of the two regions. A CT profile through these regions was fit using two error functions, each modeling the partial-volume averaging characteristics for the unmoving phantom. The motion artifact was quantified by determining the apparent distance between the two functions. The blurring artifact had a linear relationship with both the speed and the tangent of the wedge angles. When gating was employed, the blurring artifact was reduced systematically at the air-phantom interface. The gated image of phantoms moving at 20 mm/s showed similar blurring artifacts as the nongated image of phantoms moving at 10 mm/s. Nine patients were also scanned using the synchronized respiratory motion technique. Image artifacts were evaluated in the diaphragm, where high contrast interfaces intercepted the imaging plane. For patients, this respiratory gating technique reduced the blurring artifacts by 9%-41% at the lung-diaphragm interface.     

能谱CT金属伪影消除技术和骨算法成像消减脊柱金属内固定物伪影的对比研究

目的 探讨能谱CT金属伪影消除(MARs)技术与普通CT骨算法重建在消减脊柱内固定术后伪影的价值。方法 回顾性分析2013年2月—2014年2月解放军第一一七医院22例行脊柱金属内固定置入患者的影像学资料。其中男14例、女8例,年龄30～77岁。22例患者术后不同时间分别行能谱CT MARs技术检查(MARs组)、骨算法成像检查(骨算法组)。测量并计算各组感兴趣区的伪影指数(AI),采用骨窗图像质量主观评估标准对骨窗图像质量进行评分,比较MARs组和骨算法组患者骨窗图像质量评分情况,以及不同单能量值MARs组和骨算法组间AI的分布差异。结果 MARs组中,随着单能量值的减低,AI值逐渐增高。MARs组各单能量值和骨算法组间比较,AI值差异有统计学意义(F=8.696, P<0.05);进一步组间LSD-t检验显示,在60~140 keV条件下,MARs组AI值均小于骨算法组,差异均有统计学意义(P值均<0.05)。骨窗图像质量评分比较,骨算法组(2.81±0.39)分,优于MARs组(2.00±0.53)分,差异有统计学意义(t=4.856, P<0.01)。结论 能谱CT MARs技术和骨算法成像对消减脊柱金属内固定伪影各有优点,应当结合临床需要选择合适的CT成像技术。     

CT metal artifact reduction method correcting for beam hardening and missing projections

We present and validate a computed tomography (CT) metal artifact reduction method that is effective for a wide spectrum of clinical implant materials. Projections through low-Z implants such as titanium were corrected using a novel physics correction algorithm that reduces beam hardening errors. In the case of high-Z implants (dental fillings, gold, platinum), projections through the implant were considered missing and regularized iterative reconstruction was performed. Both algorithms were combined if multiple implant materials were present. For comparison, a conventional projection interpolation method was implemented. In a blinded and randomized evaluation, ten radiation oncologists ranked the quality of patient scans on which the different methods were applied. For scans that included low-Z implants, the proposed method was ranked as the best method in 90% of the reviews. It was ranked superior to the original reconstruction (p = 0.0008), conventional projection interpolation (p < 0.0001) and regularized limited data reconstruction (p = 0.0002). All reviewers ranked the method first for scans with high-Z implants, and better as compared to the original reconstruction (p < 0.0001) and projection interpolation (p = 0.004). We conclude that effective reduction of CT metal artifacts can be achieved by combining algorithms tailored to specific types of implant materials.     

MR图像Ghost伪影的校正

原始K空间奇偶回波单独重建图像数据时，根据二维多项式拟合参考扫描各点相位漂移估计值对图像进行相位校证，可减轻伪影的影响。但在噪声较严重的情况下，校正后的图像仍含有较严重的残余伪影。按相位编码方向对图像数据采用基于最小二乘法多项式拟合的方法可减轻噪声对图像的干扰，再利用二维抑制Ghost伪影算法能更有效地消除EPI成像过程中由于涡流引起的伪影。该算法的缺点是会引起图像信息强度的变化及造成图像高频信息的衰减。但由于MRI图像主要集中为低频信息，且Ghost伪影信息强度不超过图像信息最大强度20％时，该方法对图像信息的损失不会影响对病灶的识别。     

金标伪影对射波刀剂量计算及分布的影响

目的:分析金标伪影对射波刀剂量计算及分布的影响。方法:采用能谱CT的GSI扫描技术和MARS重建技术获取Lucy模体的原始CT图像和去除金标伪影后的CT图像,利用射波刀Multiplan?划系统对两组CT图像进行等中心计划设计计算,分析金标伪影对剂量计算及分布的影响。结果:金标伪影使CT图像伪影区域的CT值发生改变,最大可达63.22%,金标伪影低估了金标周围（伪影区域）正常重要组织的最大剂量值,高估了金标周围（伪影区域）正常重要组织的最小剂量值,高估了PTV的剂量覆盖率。结论:使用能谱CT可降低金标伪影对射波刀剂量计算及分布的影响。 【关键词】射波刀;金标伪影;剂量计算;剂量分布     

The reduction of artifacts due to metal hip implants in CT-attenuation corrected PET images from hybrid PET/CT scanners

CT beam hardening artifacts near metal hip implants may erroneously enhance or diminish radiotracer uptake following CT attenuation correction (AC) of PET images. An artifact reduction algorithm (ARA) was developed to reduce metal artifacts in CT-based AC-PET. The algorithm employed a Bayes classifier to identify beam-hardening artifacts, followed by a partial correction of the attenuation map. ARA was implemented on phantom and patient 18F-FDG studies using a clinical PET/CT scanner. In phantom studies ARA successfully removed two artifacts of erroneously elevated uptake near a stainless steel hip prosthesis which were depicted in the standard CT-AC PET. ARA has also identified two targets absent on the scanner PET images. Target-to-background ratios were 1.5-3 times higher for ARA-PET than scanner images. In a patient study, metal artifacts were of lower intensity in ARA-PET as compared to standard images. Potentially, ARA may improve detectability of small lesions located near metal hip implants.