Fully automated motion correction in first-pass myocardial perfusion MR image sequences

This paper presents a novel method for registration of cardiac perfusion magnetic resonance imaging (MRI). The presented method is capable of automatically registering perfusion data, using independent component analysis (ICA) to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of that ICA. This reference image is used in a two-pass registration framework. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Despite varying image quality and motion patterns in the evaluation set, validation of the method showed a reduction of the average right ventricle (LV) motion from ${1.26}pm{0.87}$ to ${0.64}pm{0.46}$ pixels. Time-intensity curves are also improved after registration with an average error reduced from ${2.65}pm {7.89}%$ to ${0.87}pm {3.88}%$ between registered data and manual gold standard. Comparison of clinically relevant parameters computed using registered data and the manual gold standard show a good agreement. Additional tests with a simulated free-breathing protocol showed robustness against considerable deviations from a standard breathing protocol. We conclude that this fully automatic ICA-based method shows an accuracy, a robustness and a computation speed adequate for use in a clinical environment.      

Classification of hyperspectral data from urban areas based on extended morphological profiles

Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.     

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.     

Hepatic perfusion imaging using factor analysis of contrast enhanced ultrasound

Contrast enhanced ultrasound imaging provides a real-time tool for evaluating vasculature in the liver. Primary liver cancer is known to be perfused exclusively by blood from the hepatic artery, whereas normal liver is also supplied by the portal vein. Visual separation of two different phases of enhancement from the independent feeding vessels is important for diagnosis but remains a challenge. This paper presents a method of using factor analysis for extracting distinct time-intensity curves. A key component to this extraction is the clustering of measured bolus curves and their projection onto a positivity domain to obtain nonnegative curves. This technique provides complementary images representing spatial loadings on each curve. As little as 1% of the data is required to contain unmixed signals to extract time-intensity curves that correlate well with true curves. A method of combining this information to display a regional hepatic perfusion image is proposed, and results are tested on a set of 10 patients. Region of interest analysis suggests it is possible to detect changes in the hepatic perfusion index of liver lesions relative to normal liver parenchyma using contrast ultrasound.      

Modeling real-time 3-d lung deformations for medical visualization.

In this paper, we propose a physics-based and physiology-based approach for modeling real-time deformations of 3-D high-resolution polygonal lung models obtained from high-resolution computed tomography (HRCT) images of normal human subjects. The physics-based deformation operator is nonsymmetric, which accounts for the heterogeneous elastic properties of the lung tissue and spatial-dynamic flow properties of the air. An iterative approach is used to estimate the deformation with the deformation operator initialized based on the regional alveolar expandability, a key physiology-based parameter. The force applied on each surface node is based on the airflow pattern inside the lungs, which is known to be based on the orientation of the human subject. The validation of lung dynamics is done by resimulating the lung deformation and comparing it with HRCT data and computing force applied on each node derived from a 4-D HRCT dataset of a normal human subject using the proposed deformation operator and verifying its gradient with the orientation.     

Inverse scattering technique applied to remote sensing of layered media

An inverse scattering technique applied to a remote estimation of the dielectric and conductivity profile of an inaccessible layered medium is presented. The inaccessible region is illuminated by plane waves at normal incident, and the data are taken as the reflected power at a fixed remote location for a set of discrete frequencies. The problem of estimating the dielectric and conductivity profile from this set of data is posed as a nonlinear integral equation. This formulation based on reflected power is appealing for practical purpose, in that the phase information of the reflected field is not required. The equation is solved by developing a quasi-Newton iterative scheme in functional space which produces a dielectric and conductivity profile that fits the data. The Backus and Gilbert resolving-power theory is used to assess the reliability of the estimates and the resolving length of the data. Results are given for the numerical reconstruction of various dielectric and conductivity profiles from an artificial data set, together with local averages estimates and resolving kernels.     

Rule-based detection of intrathoracic airway trees

New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection.     

Estimation of Local Cardiac Wall Deformation and Regional Wall Stress from Biplane Coronary Cineangiograms

A new mathematical method was developed to estimate the local epicardial deformation, wall thickness, and the regional circumferential and longitudinal wall stress using biplane coronary cineangiograms. In this method, the motion images of the coronary artery bifurcation points were used as natural landmarks for the kinematic analysis of the ventricular deformation. In four dogs and a normal patient's coronary cineangiograms, the estimation results show the validity of the present analysis, compared to the experimental results based upon the implanted markers. Thus, the present method provides a new method of evaluating the regional wall deformation and wall stress together with the blood vessel conditions using the coronary cineangiography procedure.     

基于K-medoids聚类的贝叶斯集成算法

朴素贝叶斯分类算法由于其计算高效在生活中应用广泛。本文根据集成算法的差异性特征,聚类算法聚类点的选择方式的可变性,提出了基于K-medoids聚类技术的贝叶斯集成算法,朴素贝叶斯的泛化性能得到了提升。首先,通过样本集训练出多个朴素贝叶斯基分类器模型;然后,为了增大基分类器之间的差异性,利用K-medoids算法对基分类器在验证集上的预测结果进行聚类;最后,从每个聚类簇中选择泛化性能最佳的基分类器进行集成学习,最终结果由简单投票法得出。将该算法应用于UCI数据集,并与其他类似算法进行比较可得,本文提出的基于K-medoids聚类的贝叶斯集成算法(NBKME)提高了数据集的分类准确率。     

基于粗糙集和遗传算法的知识发现方法

粗糙集理论是一种处理不确定和不精确问题的新型数学工具,为数据挖掘提供了一条崭新的途径。提出了结合粗糙集理论和遗传算法的知识发现方法。首先通过粗糙集理论对数据进行预处理,在实现属性约简中采用了遗传算法,以提高搜索效率,然后进行规则提取。以农业病虫害诊断知识发现应用为实例说明了该方法的可行性。     

The thermoacoustic method of determination of the air-tightness of metal transistors׳ packagings

The paper presents a new method of determination of the air-tightness of metal packagings of transistors. This method is based on the thermoacoustic approach with a microphone detection. The thermoacoustic approach means that the periodical overpressure is generated by the periodical temperature of the transistor caused by the periodical electric power dissipation in it. In the paper both the experimental thermoacoustic frequency characteristics and theoretical approach applied for numerical interpretations of experimental data are presented and discussed. The paper presents the experimental thermoacoustic set-up used in experiments, mathematical models, procedures applied and experimental results obtained for TO-3, TO-39 and TO-18 packagings. General predictions of the thermoacoustic theory concerning the sensitivity of this method for different types of packagings are also presented and discussed.     

A mathematical analysis on the biological zero problem in laserDoppler flowmetry

The biological zero (BZ) problem is a critical issue inherent in laser Doppler flowmetry (LDF). It causes confusion when measuring low tissue blood flows. Many experimental studies have been done on the question of whether the BZ flux should be subtracted from the normally measured flux in various situations. However this problem can only be solved after a proper mathematical analysis. Only then can one clearly define and formulate what flux is truly meaningful in blood perfusion measurement and what movement generates the BZ flux and how can one correctly remove it. Following this motivation, the movement of moving blood cells (MBCs) is decomposed into a net translation and a random wandering based on in vivo observations. This important step leads to a clear definition of the BZ and net perfusion flux and reveals that subtraction of BZ flux from the normal flux will certainly cause an underestimation of the net flux. Using this decomposition, the relationship between the net, BZ and normal flux is established which leads to the correct formula to recover the net flux from the BZ and normal fluxes. This recovered net flux is shown to be bounded by the normal flux and the normal flux minus the BZ flux. Numerical studies, preliminary phantom model and clinical evaluations manifest that the new approach is more accurate and reasonable at measuring low net fluxes. In contrast, subtracting BZ flux causes a systematic underestimation of perfusion and is apparently inappropriate even from a methodological point of view. In addition to the novel BZ solution, a general density function of the speed of MBC's is given which is more faithful than the Maxwell density used in R. Bonner and R. Nossal's paper (Appl. Opt., vol. 20, 1981). This general density function offers new possibilities for further theoretical developments in LDF     

Analysis of the distorted Born approximation for subsurface reconstruction: truncation and uncertainties effects

The problem of determining the dielectric permittivity profile of buried objects starting from the knowledge of the scattered field is considered in the two-dimensional geometry when incomplete near-zone data are collected at a single frequency under a multiview/multistatic measurement configuration. In particular, attention is paid to the practical issues of the truncated observation domain and the presence of uncertainties on data. The problem is tackled with reference to the scalar polarization by linearization of the mathematical relationship between the unknown dielectric permittivity profile and the scattered field. A homogeneous, possibly lossy, half-space geometry for the subsurface modeling is adopted, thus leading to the so-called distorted Born approximation (DBA). A thorough investigation of the class of unknown functions that can be reliably retrieved is performed by dealing with singular value decomposition of the relevant linear operator. It results that even if sources and receivers are located at the interface, a very restricted set of profile variations can be reconstructed by a stable inversion algorithm. In particular, reduced vertical features of the buried objects with respect to the horizontal ones can be reconstructed under DBA. Moreover, the truncation of the observation domain further restricts this set, affecting mainly the vertical resolution. Numerical results confirming the validity of the analysis are also provided.     

Association rule discovery with the train and test approach for heart disease prediction.

Association rules represent a promising technique to improve heart disease prediction. Unfortunately, when association rules are applied on a medical data set, they produce an extremely large number of rules. Most of such rules are medically irrelevant and the time required to find them can be impractical. A more important issue is that, in general, association rules are mined on the entire data set without validation on an independent sample. To solve these limitations, we introduce an algorithm that uses search constraints to reduce the number of rules, searches for association rules on a training set, and finally validates them on an independent test set. The medical significance of discovered rules is evaluated with support, confidence, and lift. Association rules are applied on a real data set containing medical records of patients with heart disease. In medical terms, association rules relate heart perfusion measurements and risk factors to the degree of disease in four specific arteries. Search constraints and test set validation significantly reduce the number of association rules and produce a set of rules with high predictive accuracy. We exhibit important rules with high confidence, high lift, or both, that remain valid on the test set on several runs. These rules represent valuable medical knowledge.     

Compressing image-based relighting data using eigenanalysis and wavelets

In image-based relighting (IBL) a tremendous number of reference images are needed to synthesise a high-quality novel image. This collection of reference images is referred as an IBL data set. An effective compression method for IBL data makes the IBL technique more practical. Within an IBL data set, there is a strong correlation among different reference images. In conventional eigen-based image compression methods, the principal component analysis (PCA) process is used for exploiting the correlation within a single image. Such an approach is not suitable for handling IBL data. The authors present an eigenimage-based method for compressing IBL data. The method exploits the correlation among reference images. Since there is a huge number of images and pixel values, the cascade recursive least square (CRLS) network based PCA is used to extract eigenimages. Afterwards, the wavelet approach is used for compressing those eigenimages. Simulation results demonstrate that this approach is much superior to that of compressing each reference image with JPEG and JPEG2000.     

Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging

This paper examines an alternative approach to separating magnetic resonance imaging (MRI) intensity inhomogeneity from underlying tissue-intensity structure using a direct template-based paradigm. This permits the explicit spatial modeling of subtle intensity variations present in normal anatomy which may confound common retrospective correction techniques using criteria derived from a global intensity model. A fine-scale entropy driven spatial normalisation procedure is employed to map intensity distorted MR images to a tissue reference template. This allows a direct estimation of the relative bias field between template and subject MR images, from the ratio of their low-pass filtered intensity values. A tissue template for an aging individual is constructed and used to correct distortion in a set of data acquired as part of a study on dementia. A careful validation based on manual segmentation and correction of nine datasets with a range of anatomies and distortion levels is carried out. This reveals a consistent improvement in the removal of global intensity variation in terms of the agreement with a global manual bias estimate, and in the reduction in the coefficient of intensity variation in manually delineated regions of white matter.     

Quantification of Regional Myocardial Perfusion by Exponential Infusion of 11C-Butanol

This study was designed to determine whether regional myocardial perfusion can be assessed quantitatively in vivo in dogs by administration of a freely diffusible tracer intravenously at an exponentially increasing-rate. Previously, we reported the mathematical basis of the approach and its use for assessment of global perfusion in isolated hearts. In this study, computer simulations demonstrated that inter-regional diffusion of tracer perturbs results by no more than 7 percent under worst case conditions. Regional myocardial blood flow assessed in open chest dogs with myocardial activity deternined by well counting by the exponential infusion method correlated closely to values obtained with radiolabeled microspheres (r = 0.986, n = 35 determinations). Thus, regional myocardial perfusion can be quantified with an infusion technique potentially applicable to the tomographic characterization of impaired perfusion in experimental animals and ultimately in patients with positron emission tomography.     

Airplane attitude estimation using computer vision: simple method and actual experiments

The development, implementation, and validation of a simple algorithm for airplane attitude estimation, based on a video of the horizon obtained from a camera mounted on the plane, is presented. The method provides absolute attitude estimation, which makes it suitable for the calibration and testing of artificial horizon systems. Solutions for some of the difficulties encountered in the determination of roll and pitch angles are discussed and presented. Experimental results from actual flights in a light aircraft are used to compare estimates obtained by the proposed method with data obtained from a commercial attitude reference system.     

基于U＿net网络的单幅全息图重建方法研究

全息重建方法是全息成像技术和全息术的一个核心部分。本文提出了一种基于U_net网络的单幅全息图重建方法。首先使用基于马赫-增德尔干涉仪的光路结构全息实验成像系统记录全息图,将得到的全息图作为训练集,然后采用训练集对U_net网络进行训练,在经过训练后得到全息图的数学模型,用该数学模型便可以对全息图进行重建。仿真和实验结果表明,基于U_net网络的单幅全息图重建方法计算速度更快还只需要更少的测量数据,并且仅使用单幅全息图就可以重建图像,且重建图像没有直流项和孪生项的干扰。     

About the geometry of asymmetric fiber orientation distributions

Fiber orientation distributions (FODs) based on diffusion-sensitized magnetic resonance imaging are usually symmetric, primarily due to the nature of the diffusion. In contrast, the underlying fiber configurations are not, as bending or fanning configurations are inherently asymmetric. We propose to dismiss the symmetry of the FOD to additionally encode the asymmetry of the underlying fiber configuration. This is of particular importance for low resolution images that are common in diffusion weighted imaging. We set up the mathematical foundations and geometric interpretations of asymmetric FODs and show how one can benefit from these considerations. We infer a continuity condition that is used as a prior during FOD estimation by constrained spherical deconvolution. This new prior shows superior performance in comparison to other spatial regularization strategies in reliability and accuracy.