基于非广延熵先验的PET图像重建EI北大核心CSCD

最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。     

基于非广延熵先验的PET图像重建

最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。     

采用MRF二次混合多阶先验的PET图像的贝叶斯重建

在正电子发射成像（肿）中，很多方法被用来抑制重建图像中的噪声效果，其中。在所有方法中，贝叶斯重建或者最大化后验估计的方法被证明具有在重建图像质量方面相对于其他方法的优越性。基于贝叶斯重建，本研究提出了一种应用于贝叶斯重建中新的综合了二次一阶先验和二次二阶先验的马尔可夫随机场混合多阶先验。基于不同阶数的二次平滑先验的自身的不同性质，该新先验的设计的目的是实现自适应的发挥这些算子的作用。该混合先验能够保持其先验能量函数凸性，从而保证整体目标贝叶斯后验能量函数的凹性。模拟实验和实验结果的比较证明了对于PET重建，该先验在抑制背景噪声和保持边缘方面均具有很好的表现。     

结构光方法在荧光分子断层成像系统的三维轮廓重构中的应用

荧光分子断层成像系统需要获取成像物体的三维表面轮廓,用于荧光团浓度重建的前向模型,本文提出了基于结构光方法的成像物体的三维表面轮廓重构。首先利用8比特格雷码对图像进行编码,再将编码后的图像投射到成像对象,这时经成像物体的三维表面轮廓调制后的图像再通过二值化、解码、插值和轮廓重构等步骤最后输出三维表面轮廓。圆柱物体和小鼠实验表明,该方法可以较好地重构成像物体的三维表面轮廓,并且具有硬件系统简单、易实现等优点。     

节点反投影方法在三维EIT模型中的仿真与实验研究

电阻抗成像(EIT)技术中逆问题的病态特性是造成重建图像分辨率较低的主要原因之一,增加先验信息是改善成像效果的可行方法.建立三维圆柱体仿真模型,对边界电压数据进行多项式曲面拟合,增加先验信息,采用节点反投影方法进行图像重建.对两种目标模型进行仿真实验,并利用本实验室设计的128通道EIT系统进行水槽物理模型重建实验.仿真实验结果表明,较之传统的反投影方法,两种目标模型采用节点反投影方法重建的总体误差分别降低了8.87％和6.85％;在物理模型中,重建图像可清晰显示目标物体.所提出的方法有望用于提高临床检测与监护的成像质量.     

傅立叶轮廓术在重建三维物体轮廓中的应用

随着荧光分子断层成像(FMT)技术中系统结构和重建算法的改进,FMT技术已经成为一种重要的生命医学研究手段。目前FMT算法都需要得到被测对象的三维表面轮廓作为边界约束条件。本文提出一种在蓝紫光条件下基于傅立叶变换轮廓术(FTP)方法,经图像处理、频谱分析及滤波后重构出FMT系统实验中物体的轮廓。实验结果表明,该方法具有毫米量级的重建精度。该方法能够很好地重建出物体表面轮廓,以及实时监控实验过程中物体的行为,保证了整个成像过程的对应性。此外,该法选用蓝紫光段,避免了对荧光成像的干扰。     

基于GMRES和Tikhonov正则化的生物电阻抗图像重建算法

电阻抗层析成像(Electrical impedance tomography,EIT)是利用被测物体场内部电导率分布不均匀性,通过边界注入电流,测量边界电压变化,重构被测场内电导率分布图像。由于EIT测量数据有限,场域存在严重的非线性,导致问题的欠定性。我们介绍了一种新的组合算法,利用GMRES算法生成Krylov子空间,并结合Tik-honov正则化方法进行图像重建。该算法不仅改善了实时性,而且提高了成像质量及鲁棒性。     

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。     

前列腺肿瘤单方向X射线成像定位的算法比较

目的：利用单方向x射线成像定位算法来实时定位肿瘤靶区可以提高放射治疗精度，应用简便的同时能够降低对病人曝光的成像剂量。但是由于缺少沿射线束方向上的运动信息，如何有效地利用之前的运动信息来保证定位精度是需要解决的一个问题。方法：针对前列腺肿瘤，选择4种典型的实时定位算法，并使用十位患者的Calypso磁场定位数据进行模拟定位来比较它们的定位效果。这四种定位算法分别是仅分布图法、两种基于高斯概率密度分布的算法和贝叶斯概率密度分布法。‘结果：仅分布图法的rmse（均方根误差）小于5mm，但是最大误差能达到50mm；高斯概率分布法1的rmse小于2．6mm，最大误差小于6mm；高斯概率分布法2的定位结果中多数病人的rmse小于1．5mm，最大误差小于7mm，而有部分病人的rmse大于4．5mm．最大误差大于30mm；贝叶斯概率密度分布法的rmse小于2．5mm，最大误差小于8．8mnl。结论：高斯概率密度分布法1和贝叶斯概率密度分布法相对优于其它两种；尤其是贝叶斯概率密度分布法能够最好地适用于各种类型的前列腺肿瘤运动的实时定位。     

测量距离和角度与喉动态镜图像关系的实验研究

探讨喉动态镜成像大小与被检测对象间距离及角度的关系及其密切程度。使用基于虚拟仪器的喉动态观测系统,测量大小固定的物体,取不同距离、不同角度时所呈现图像的面积与长度。结果：当被测物体大小固定时,测量距离对被测物体所呈现的喉镜图像的面积和长度有显著影响,而测量角度对被测物体所呈现的喉镜图像的面积和长度无显著影响。     

Structured Incorporation of Prior Information in Relationship Identification Problems

The objective of this paper is to show how various sources of information can be modelled and integrated to address relationship identification problems. Applications come from areas as diverse as evolution and conservation research, genealogical research in human, plant and animal populations, and forensic problems including paternity cases, identification following disasters, family reunions and immigration issues. We propose assigning a prior probability distribution to the sample space of pedigrees, calculating the likelihood based on DNA data using available software and posterior probabilities using Bayes' Theorem. Our emphasis here is on the modelling of this prior information in a formal and consistent manner. We introduce the distinction between local and global prior information, whereby local information usually applies to particular components of the pedigree and global prior information refers to more general features. When it is difficult to decide on a prior distribution, robustness to various choices should be studied. When suitable prior information is not available, a flat prior can be used which will then correspond to a strict likelihood approach. In practice, prior information is often considered for these problems, but in a generally ad hoc manner. This paper offers a consistent alternative. We emphasise that many practical problems can be addressed using freely available software.     

Bayesian integration in force estimation

When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.     

NEURAL FOUNDATIONS FOR UNDERSTANDING SOCIAL AND MECHANICAL CONCEPTS

Motivated by neuropsychological investigations of category-specific impairments, many functional brain imaging studies have found distinct patterns of neural activity associated with different object categories. However, the extent to which these category-related activation patterns reflect differences in conceptual representation remains controversial. To investigate this issue, functional magnetic resonance imaging (fMRI) was used to record changes in neural activity while subjects interpreted animated vignettes composed of simple geometric shapes in motion. Vignettes interpreted as conveying social interactions elicited a distinct and distributed pattern of neural activity, relative to vignettes interpreted as mechanical actions. This neural system included regions in posterior temporal cortex associated with identifying human faces and other biological objects. In contrast, vignettes interpreted as conveying mechanical actions resulted in activity in posterior temporal lobe sites associated with identifying manipulable objects such as tools. Moreover, social, but not mechanical, interpretations elicited activity in regions implicated in the perception and modulation of emotion (right amygdala and ventromedial prefrontal cortex). Perceiving and understanding social and mechanical concepts depends, in part, on activity in distinct neural networks. Within the social domain, the network includes regions involved in processing and storing information about the form and motion of biological objects, and in perceiving, expressing, and regulating affective responses.     

Measures of performance in nonlinear estimation tasks: prediction of estimation performance at low signal-to-noise ratio

Maximum-likelihood (ML) estimation is an established paradigm for the assessment of imaging system performance in nonlinear quantitation tasks. At high signal-to-noise ratio (SNR), ML estimates are asymptotically Gaussian-distributed, unbiased and efficient, thereby attaining the Cramer-Rao bound (CRB). Therefore, at high SNR the CRB is useful as a predictor of the variance of ML estimates and, consequently, as a basis for measures of estimation performance. At low SNR, however, the achievable parameter variances are often substantially larger than the CRB and the estimates are no longer Gaussian-distributed. These departures imply that inference about the estimates that is based on the CRB and the assumption of a normal distribution will not be valid. We have found previously that for some tasks these effects arise at noise levels considered clinically acceptable. We have derived the mathematical relationship between a new measure, chi2(pdf-ML), and the expected probability density of the ML estimates, and have justified the use of chi2(pdf-ML)-isocontours in parameter space to describe the ML estimates. We validated this approach by simulation experiments using spherical objects imaged with a Gaussian point spread function. The parameters, activity concentration and size, were estimated simultaneously by ML, and variances and covariances calculated over 1000 replications per condition from 3D image volumes and from 2D tomographic projections of the same object. At low SNR, where the CRB is no longer achievable, chi2(pdf-ML)-isocontours provide a robust prediction of the distribution of the ML estimates. At high SNR, the chi2(pdf-ML)-isocontours asymptotically approach the analogous chi2(pdf-F)-contours derived from the Fisher information matrix. The chi2(pdf-ML) model appears to be suitable for characterization of the influence of the noise level and characteristics, the task, and the object on the shape of the probability density of the ML estimates at low SNR. Furthermore, it provides unique insights into the causes of the variability of estimation performance.     

Neural foundations for understanding social and mechanical concepts

Motivated by neuropsychological investigations of category-specific impairments, many functional brain imaging studies have found distinct patterns of neural activity associated with different object categories. However, the extent to which these category-related activation patterns reflect differences in conceptual representation remains controversial. To investigate this issue, functional magnetic resonance imaging (fMRI) was used to record changes in neural activity while subjects interpreted animated vignettes composed of simple geometric shapes in motion. Vignettes interpreted as conveying social interactions elicited a distinct and distributed pattern of neural activity, relative to vignettes interpreted as mechanical actions. This neural system included regions in posterior temporal cortex associated with identifying human faces and other biological objects. In contrast, vignettes interpreted as conveying mechanical actions resulted in activity in posterior temporal lobe sites associated with identifying manipulable objects such as tools. Moreover, social, but not mechanical, interpretations elicited activity in regions implicated in the perception and modulation of emotion (right amygdala and ventromedial prefrontal cortex). Perceiving and understanding social and mechanical concepts depends, in part, on activity in distinct neural networks. Within the social domain, the network includes regions involved in processing and storing information about the form and motion of biological objects, and in perceiving, expressing, and regulating affective responses.     

An fMRI study of episodic memory: retrieval of object, spatial, and temporal information

Sixteen participants viewed a videotaped tour of 4 houses that highlighted a series of objects and their spatial locations. Participants were tested for memory of object, spatial, and temporal-order information while undergoing functional magnetic resonance imaging. Preferential activation was observed in the right parahippocampal gyrus during the retrieval of spatial-location information. Retrieval of contextual information (spatial location and temporal order) was associated with activation in the right dorsolateral prefrontal cortex. In bilateral posterior parietal regions, greater activation was associated with processing of visual scenes regardless of the memory judgment. These findings support current theories positing roles for frontal and medial temporal regions during episodic retrieval and suggest a specific role for the hippocampal complex in the retrieval of spatial-location information.     

MRI prior computation and parallel tempering algorithm: a probabilistic resolution of the MEG/EEG inverse problem

Since the MEG inverse problem is ill-posed and admits many possible solutions, it is not possible to give it a single " true" answer. Therefore, we propose here to use a specific probabilistic algorithm to map the full probability distribution of the MEG sources with Markov Chain Monte Carlo methods. Using a Bayesian approach, the probability of the MEG solutions is expressed as the product of the likelihood by the prior probability. To compute the prior and constrain the MEG inverse problem resolution, MRI data are also acquired and automatically processed to determine the brain position and volume. We then use Parallel Tempering algorithm to estimate the full posterior probability and determine the likely solutions of the inverse problem. We illustrate the method with results obtained from the analysis of somatosensory data. This illustrates both the MRI processing for the prior computation, and how the knowledge of the full posterior probability distribution can be used to estimate the position of the sources, as well as their likely extension.     

HOW IS THE FUSIFORM GYRUS RELATED TO CATEGORY-SPECIFICITY?

There is growing evidence from functional imaging studies that distinct regions in the fusiform gyri are differentially sensitive to object category. In this paper, we investigate how the areas that are more sensitive to animals than tools respond to other visual and semantic variables. We illustrate that (1) category effects in the fusiform areas are stronger for pictures of objects than their written names; (2) retrieving information on the colour or size of objects activates a left lateralised fusiform area that lies anterior to the category-sensitive areas; and (3) both left and right category-sensitive areas respond strongly to visual feature detection on false fonts—meaningless visual stimuli with no semantic associations. These results dissociate the responses in two fusiform areas: The posterior category-sensitive areas are primarily modulated by visual input, whereas a more anterior polymodal region is involved in the retrieval of visual information. In addition, we demonstrate that the posterior areas which are more active for animals than tools are also more active for fruits than tools. Our data are therefore consistent with the proposal that activation in the lateral posterior fusiform gyri reflects the demands on structural differentiation. Since animals and fruits tend to have more structurally similar neighbours than man-made kinds of objects, category effects are likely to be observed during most picture identification tasks. In contrast, when the stimuli are written or auditory names, category effects may only be observed when the task requires access to fine spatial details in the objects' structures.     

Evanescent Electromagnetics: A Novel, Super-Resolution, and Non-Intrusive, Imaging Technique for Biological Applications

Scanning tunneling and atomic force microscopes (STM and AFM) are used to study biological materials. These methods, often capable of achieving atomic resolutions, reveal fascinating information regarding the inner workings of these materials. However, both STM and AFM require physical contact to the specimen. In the case of STM, the specimen needs to be conducting as well. Here we introduce a new method for imaging biological materials through air or a suitable liquid using decaying or evanescent fields at the tip of a properly designed microwave resonator. This novel method involves the use of an evanescent microwave probe (EMP) and is capable of imaging a variety of non-uniformities in biological materials including conductivity, permittivity, and density variations. EMP is a non-contact and non-destructive sensor and it does not require conducting specimens. Its spatial resolution is currently around 0.4 m at 1 GHz. We have used this probe to map non-uniformities in a variety of materials including metals, semiconductors, insulators, and biological and botanical samples. Here we discuss applications of EMP imaging in bone, teeth, botanical, and agricultural specimens.     

How is the fusiform gyrus related to category-specificity?

There is growing evidence from functional imaging studies that distinct regions in the fusiform gyri are differentially sensitive to object category. In this paper, we investigate how the areas that are more sensitive to animals than tools respond to other visual and semantic variables. We illustrate that (1) category effects in the fusiform areas are stronger for pictures of objects than their written names; (2) retrieving information on the colour or size of objects activates a left lateralised fusiform area that lies anterior to the category-sensitive areas; and (3) both left and right category-sensitive areas respond strongly to visual feature detection on false fonts-meaningless visual stimuli with no semantic associations. These results dissociate the responses in two fusiform areas: The posterior category-sensitive areas are primarily modulated by visual input, whereas a more anterior polymodal region is involved in the retrieval of visual information. In addition, we demonstrate that the posterior areas which are more active for animals than tools are also more active for fruits than tools. Our data are therefore consistent with the proposal that activation in the lateral posterior fusiform gyri reflects the demands on structural differentiation. Since animals and fruits tend to have more structurally similar neighbours than man-made kinds of objects, category effects are likely to be observed during most picture identification tasks. In contrast, when the stimuli are written or auditory names, category effects may only be observed when the task requires access to fine spatial details in the objects' structures.