结合各向异性扩散滤波Thin Plate先验正电子发射断层图像重建的算法

背景:在正电子发射断层成像中,MAP重建方法通过引入先验分布约束,可以明显提高重建图像的质量,但不合适的先验分布项可能会造成重建图像过度平滑或出现阶梯状边缘伪影.目的:针对基于传统局部先验信息的MAP方法易于导致重建图像过平滑或产生阶梯状边缘伪影的问题,提出了一种结合各向异性扩散滤波的、基于Thin Plate先验的改进MAP重建算法.方法:重建算法由两步组成:基于双向扩散系数的PDE各向异性扩散滤波和基于Thin Plate先验的MAP估计.重建图像通过这两步交替迭代得到.文中采用归一化均方根误差和信噪比定量评价重建图像质量.结果与结论:结合了基于双向扩散系数的PDE各向异性扩散滤波,并将Thin Plate二次二阶先验模型引入到MAP重建算法中,所获得的重建结果图像在抑制噪声、边缘保持方面取得了良好的效果,SNR、RMSE以及视觉评价等方面均有较大程度的改善.     

正电子发射断层图像重建中基于有序子集惩罚最小二乘算法的应用

为了解决正电子断层成像中传统迭代重建算法不能有效抑制噪声和收敛速度慢的问题,在最小二乘算法中引入二次平滑先验惩罚项,并结合有序子集加速方法用于正电子发射断层图像重建,形成了有序子集惩罚最小二乘算法.实验结果表明,有序子集惩罚最小二乘算法相对于最大似然估计等算法,不仅能够有效地抑制噪声,重建出质量更好的图像,而且较大地提高了收敛速度.     

正电子发射型计算机断层显像检查患者的护理

目的:探讨正电子发射计算机断层显像检查中的护理方法。方法:对201例患者在进行正电子发射计算机断层显像检查的全过程,实施耐心、细致、周到的护理服务。结果:所有患者均顺利完成正电子发射计算机断层显像检查,所得正电子发射计算机断层显像图像全部达到了临床诊断要求。结论:全程、细致、耐心、周到的护理服务是正电子发射计算机断层显像检查中的一个重要环节。     

小波先验在OSL重建算法中的应用

背景：MAP（最大后验）统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的。目的：根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声。方法：实验从“变换域”的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建。结果与结论：基于小波先验的OSL算法比ML—EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚。     

小波先验在OSL重建算法中的应用

背景:MAP(最大后验)统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的.目的:根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声.方法:实验从“变换域"的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建.结果与结论:基于小波先验的OSL算法比ML-EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚.     

基于list-mode数据重建PET图像的研究进展

本文通过对基于list-mode和sinogram两种数据形式的PET图像EM重建算法的比较，从几个方面介绍了直接采用list-mode数据进行PET图像重建的优点，并对基于list-mode的PET图像重建算法的发展动态进行了评述。     

正电子发射型断层显像仪的临床应用

正电子发射型断层显像仪（PET）是利用发射正电子的放射性核索及其标记化合物为显像剂对脏器或组织进行功能、代谢和分子成像的仪器。广泛应用于肿瘤、神经与精神疾病以及心血管等疾病的诊断、治疗与研究中，是本世纪最重要的成像设备。     

正电子发射型计算机断层成像在甲状腺肿瘤原发灶的诊断有效性研究

目的：研究甲状腺肿瘤原发灶诊断中采用正电子发射型计算机断层成像（positron emission tomography/computer tomography,PET/CT）技术后的诊断效果。方法：选择2018年1月—2020年12月期间于云南省肿瘤医院接受甲状腺肿瘤原发灶PET/CT诊断的100例患者,以病理结果作为金标准,观察诊断结果、数据分析结果、诊断时间及费用,分析其有效性。结果：PET/CT检出率与金标准一致,达到100.00%,PET/CT诊断恶性33例（33.00%）、良性67例（67.00%）;金标准诊断恶性34例（34.00%）、良性66例（66.00%）,PET/CT诊断甲状腺肿瘤良恶性与金标准诊断结果差异不显著（P> 0.05）;PET/CT诊断真阳性33例（33.00%）、假阳性0例（0.00%）、真阴性66例（66.00%）、假阴性1例（1.00%）,诊断灵敏度为97.05%,特异度100.00%,准确性99.00%,误诊率0.00%,漏诊率2.94%,阳性预测值100.00%,阴性预测值98.50%,假阳性率0.00%,假阴性率2.94%,正确指数9...     

胃癌患者行正电子发射型计算机断层成像检查的护理体会

正电子发射断层显像／X线计算机体层成像（positron emission tomography／computed tomography，PET／CT）作为当今医疗影像学中最先进的设备，融合了PET的功能图像，又兼顾了CT的解剖图像，使两者合二为一，产生高清晰的融合影像，并通过智能化合成分析，有效降低了PET或CT的误差。PET／CT应用最为广泛而成熟的领域是恶性肿瘤的诊断，尤其在胃癌早期诊断中有积极的作用。[第一段]     

胃癌患者行正电子发射型计算机断层成像检查的护理体会

正电子发射断层显像/X线计算机体层成像(positron e- mission tomography/computed tomography,PET/CT)作为当今医疗影像学中最先进的设备,融合了PET的功能图像,又兼顾了CT的解剖图像,使两者合二为一,产生高清晰的     

Advanced Markov random field model based on local uncertainty for unsupervised change detection

Markov random field (MRF)-based methods are effective and popular unsupervised methods for detecting changes in remotely sensed images. In this method, the spatial contextual information is well utilized to conquer the problem of noise sensitivity in the pixel-wise change detection methods. Meanwhile, MRF also suffers from the over-smooth problem and the hard balance between denoising and detail preserving. To tackle these limitations, this letter presented an advanced MRF model based on local uncertainty (LUMRF). First, fuzzy c-means (FCM) cluster method is applied to the difference image obtained by change vector analysis to character each pixel with an initial label (change or no-change) and the corresponding membership values. To improve the detail preservation ability of MRF, the local uncertainty in a given window is subsequently computed and then integrated in the spatial energy term of MRF model. Finally, a refined change map is produced by the proposed LUMRF method. Two experiments were conducted to evaluate the effectiveness of the proposed method. The results show that, in comparison to FCM and MRF, LUMRF gives a better performance with the lowest total error detection and the performance is more robust to the parameter changes.     

基于交替投影的CT图像重建算法

目的 探讨基于交替投影的CT图像重建算法的可行性。方法 将CT图像的重建转化为凸集优化问题,将重建模型分解为多个约束,并确定其对应的凸集,通过交替投影的方式在其交集中找到可行解。对TV先验项构成的凸集的求解,通过定义TV函数的上方图集,利用点到这个凸集的切向超平面的连续投影,找到起始点到TV凸集最近的点。分别采用CPTV算法、TV-POCS算法和基于交替投影的CT图像重建算法对Shepp-Logan头部图像进行重建,比较不同算法对不同角度投影图像重建后的均方根误差（RMSE）。分别采用TV-POCS算法和基于交替投影的CT图像重建算法对小鼠脊椎轴位图像进行重建,比较两种算法的归一化均方距离（d）和归一化平均绝对距离（r）。结果 CPTV算法所重建的图像平滑性较差,伪影较多,而TV-POCS算法和基于交替投影的重建算法不仅有效抑制了噪声,还保护了图像的边缘,图像质量较高。基于交替投影的重建算法的RMSE比另外两种算法下降速度更快,收敛值更小。基于交替投影的重建算法重建图像的d和r值均小于TV-POCS算法（0.064 0 vs 0.262 4,0.073 7 vs 0.298 2）。结论 采用基于交替投影的重建算法重建有限角度的CT投影图像不需参数估计,且图像质量更高,收敛速度更快。     

Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

The construction of subject-specific dense and realistic 3D meshes of the myocardial fibers is an important pre-requisite for the simulation of cardiac electrophysiology and mechanics. Current diffusion tensor imaging (DTI) techniques, however, provide only a sparse sampling of the 3D cardiac anatomy based on a limited number of 2D image slices. Moreover, heart motion affects the diffusion measurements, thus resulting in a significant amount of noisy fibers. This paper presents a Markov random field (MRF) approach for dense reconstruction of 3D cardiac fiber orientations from sparse DTI 2D slices. In the proposed MRF model, statistical constraints are used to relate the missing and the known fibers, while a consistency term is encoded to ensure that the obtained 3D meshes are locally continuous. The validation of the method using both synthetic and real DTI datasets demonstrates robust fiber reconstruction and denoising, as well as physiologically meaningful estimations of cardiac electrical activation.     

Adaptive superpixel based Markov random field model for unsupervised change detection using remotely sensed images

This study presents an adaptive superpixel based Markov Random Field (ASP_MRF) model for unsupervised remotely sensed images change detection. Firstly, the difference image is generated by change vector analysis (CVA) and the zero parameter version of the ‘simple linear iterative clustering’ method (SLICO) is applied on the difference image to obtain the superpixel map. Then, the superpixel map is initially labeled as changed and unchanged class by Fuzzy c-means (FCM) clustering method. Thirdly, the region adjacent graph (RAG) is built on the superpixel map to model the spatial constraints between the adjacent superpixels. Specially, the spectral dissimilarity between the adjacent superpixels and the label fuzziness of the neighbored superpixels were incorporated in the RAG. Lastly, The initial labels of the superpixel map are iteratively refined with ASP_MRF to generate the final change map. The experimental results prove that ASP_MRF obtained the most accurate change map and outperformed the results by pixel level MRF and superpixel based MRF, which verifies the effectiveness of ASP_MRF.     

Class-specific weighting for Markov random field estimation: Application to medical image segmentation

Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: (1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, (2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and (3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation—achieved via the incorporation of multiplicative weights into the MAP cost function—which allows certain classes to be preferred over others. This creates a natural bias for specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the MPM cost function (in place of the strategy we presented previously), yielding multiplicative weighted MPM (MWMPM) estimation. Furthermore, we describe how MWMAP and MWMPM can be implemented using adaptations of current estimation strategies such as iterated conditional modes and MPM Monte Carlo. To illustrate these implementations, we first integrate them into two separate MRF-based classification systems for detecting carcinoma of the prostate (CaP) on (1) digitized histological sections from radical prostatectomies and (2) T2-weighted 4 Tesla ex vivo prostate MRI. To highlight the extensibility of MWMAP and MWMPM to estimation tasks involving more than two classes, we also incorporate these estimation criteria into a MRF-based classifier used to segment synthetic brain MR images. In the context of these tasks, we show how our novel estimation criteria can be used to arbitrarily adjust the sensitivities of these systems, yielding receiver operator characteristic curves (and surfaces).     

Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images

Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.OCIS codes: (100.0100) Image processing, (110.4500) Optical coherence tomography, (330.6130) Spatial resolution     

A one step image reconstruction algorithm for electrical impedance tomography in three dimensions

The move from two to three dimensions in the study of electrical impedance tomography (EIT) has generated a great increase in computational demands. It is therefore interesting to investigate ways in which this demand can be reduced, and in this paper we have presented some results of one such approach. The NOSER algorithm was introduced some years ago and we have extended it to include more realistic electrode models. The main feature of the method is that by starting from a uniform conductivity distribution many quantities can be pre-calculated.