基于非高斯分布模型扩散加权成像在肝细胞癌中的研究进展

扩散加权成像(diffusion weighted imaging,DWI)已经在肝细胞癌(hepatocellularcarcinoma,HCC)中广泛应用。由于传统DWI难以准确反映偏离高斯分布的水分子扩散信息,越来越多的研究致力于探讨非高斯分布模型DWI在HCC中的作用。此成像技术应用从神经系统拓展到体部,目前部分已应用于HCC的诊断与鉴别诊断、病理分类、分级、评价治疗反应、预后评估等方面。基于此,本文就非高斯分布模型DWI在HCC中的研究现状及技术原理进行总结,旨在进一步分析先进的扩散技术在HCC应用中存在的挑战,推动非高斯分布模型DWI在HCC中的进一步应用和发展。此外还将对未来的研究方向展开探讨。

多种非高斯分布模型扩散加权成像对大鼠肝脏缺血再灌注模型的评价价值

目的探讨联合多种非高斯分布模型DWI评价大鼠肝脏缺血再灌注损伤(HIRI)模型的价值。方法SD大鼠42只,采用随机数字表法分为7组,每组6只,分别为HIRI后6h、12h、1d、3d、7d、14d组和对照组。实验组行肝右叶门静脉和肝动脉结扎3h后再灌注,分别在HIRI后6h、12h、1d、3d、7d、14d进行MRI扫描(T1WI、T2WI、低b值DWI以及超高b值DWI),对照组行假手术后行MRI扫描。根据单指数模型、双指数模型、三指数模型、拉伸指数模型的DWI以及扩散峰度成像得出多个参数,并观察各时间点其动态变化,参数包括标准ADC值(ADCs)、纯扩散系数(D)、伪扩散系数(D*)、灌注分数(f)、超高ADC值(ADCu)、扩散分布指数(DDC)、扩散异质性指数(α)、平均扩散系数(MD)及平均峰度系数(MK)。所有大鼠在MRI扫描结束后于肝右叶取材,行HE染色观察病理表现。应用单因素方差分析比较多组之间各参数(ADCs、D、D^*、f、ADCu、α、DDC、MD、MK)差异。结果所有大鼠均顺利完成了MRI检查及病理检查。术后6h开始大鼠肝右叶T1WI呈稍低信号,T2WI呈高信号。肝右叶病变区域在术后1周明显变小,术后2周基本消失,低b值DWI以及超高b值DWI表现与T2WI基本一致。HIRI后6h、12h、1d、3d、7d、14d组和对照组间的各参数的差异均有统计学意义(P均<0.05)。其中HIRI后12h组、1d组、3d组分别与HIRI后6h组、7d组、14d组、对照组组间ADCs差异有统计学意义(P<0.05);HIRI后6h组、12h组、1d组、3d组、7d组分别与HIRI后14d组、对照组组间D、D^*、f、ADCu、α、DDC、MK差异有统计学意义(P<0.05),HIRI后1d组、3d组分别与HIRI后6h组、12h组、7d组、14d组、对照组组间D差异有统计学意义(P<0.05);HIRI后6h组、12h组、1d组、3d组分别与HIRI后7d组、14d组、对照组组间f差异有统计学意义(P<0.05);HIRI后12h组与HIRI后14d组、对照组组间MD差异有统计学意义(P<0.05),HIRI后1d组与HIRI后14d组组间MD差异有统计学意义(P<0.05)。结论多种非高斯分布模型DWI评价大鼠HIRI模型优于常规DWI。

基于随机经济模型预测控制的电热综合能源系统运行优化

北方供暖地区存在大量热电联产机组,其“以热定电”的运行特性限制了机组调峰能力。同时,随着我国能源替代的提出以及风电的快速发展,建立包含风电的电热综合能源系统是降低电热耦合以及系统运行成本的重要手段。因此,建立电热综合能源系统模型,在满足用户电热负荷需求的基础上,综合考虑风电不确定性以及系统动态过程中的经济性,提出一种基于情景树结构的随机经济模型预测控制策略。为了降低风电功率随机性干扰和风电功率变化难以描述的问题,提出采用一种改进的非线性变换方法建立风电功率非高斯分布模型,在此基础上建立基于情景树结构的风电功率变化显式表达结构,在保证鲁棒性的同时降低了优化过程中风电随机性的干扰。最后,通过累计节约成本的复合单步增长率指标验证所提策略的有效性。

复小波包变换域混合统计模型图像降噪算法

该方法利用四树复小波包变换具有的移不变性、良好的方向选择性和对高频信号的细致分析能力等特点,把含噪图像分解成低频逼近子图和若干高频方向子图;在保留低频逼近子图复系数不变的同时,利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类.对主要类和次要类复系数分别进一步采用非高斯双变量模型和零均值高斯分布模型进行噪声抑制.实验结果表明,无论是峰值信噪比(PSNR)指标,还是在视觉效果上,本文方法的去噪性能均好于传统的双树复小波变换去噪、四树复小波包变换去噪和小波域高斯尺度混合模型去噪,在有效抑制噪声的同时,具有很好的图像边缘和细节保护能力.

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Non-gaussian Test Models for Prediction and State Estimation with Model Errors

Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiqui- tous in applications in contemporary science and engineering where the statistical ensemble prediction and the real time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test models have a crucial role to provide firm mathematical underpinning or new algorithms for vastly more complex scien- tific phenomena. Here, a class of statistically exactly solvable non-Gaussian test models is introduced, where a generalized Feynman-Ka~ formulation reduces the exact behavior of conditional statistical moments to the solution to inhomogeneous Fokker-Planck equations modified by linear lower order coupling and source terms. This procedure is applied to a test model with hidden instabilities and is combined with information theory to address two important issues in the contemporary statistical prediction of turbulent dynamical systems： the coarse-grained ensemble prediction in a perfect model and the improving long range forecasting in imperfect models. The models discussed here should be use- ful for many other applications and algorithms for the real time prediction and the state estimation.