A model for simulation and patient-specific visualization of the tissue volume of influence during brain microdialysis

Microdialysis can be used in parallel to deep brain stimulation (DBS) to relate biochemical changes to the clinical outcome. The aim of the study was to use the finite element method to predict the tissue volume of influence (TVI_max) and its cross-sectional radius (r _TVImax) when using brain microdialysis, and visualize the TVI_max in relation to patient anatomy. An equation based on Fick’s law was used to simulate the TVI_max. Factorial design and regression analysis were used to investigate the impact of the diffusion coefficient, tortuosity and loss rate on the r _TVImax. A calf brain tissue experiment was performed to further evaluate these parameters. The model was implemented with pre-(MRI) and post-(CT) operative patient images for simulation of the TVI_max for four patients undergoing microdialysis in parallel to DBS. Using physiologically relevant parameter values, the r _TVImax for analytes with a diffusion coefficient D = 7.5 × 10⁻⁶ cm²/s was estimated to 0.85 ± 0.25 mm. The simulations showed agreement with experimental data. Due to an implanted gold thread, the catheter positions were visible in the post-operative images. The TVI_max was visualized for each catheter. The biochemical changes could thereby be related to their anatomical origin, facilitating interpretation of results.     

一种文本数据挖掘与可视化的新方法

传统搜索引擎的搜索结果采用的是以一维列表的形式展现,随着文献数据的急剧增多,用户对于搜索结果的辨识和分析速度也在急剧下降.为了弥补传统工具的这一不足,本文开发了"语义图"(semantic map,SMAP),此工具对文献数据进行数据挖掘和可视化,包括关联匹配和聚类,将搜索结果以二维矩阵图的形式展示出来,方便用户理解数据之间的内部联系,并帮助用户迅速从整体上把握搜索结果.最后以蛋白质组学文献分析过程为例具体展示了此工具的应用.     

Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples

Gene expression data are the representation of nonlinear interactions among genes and environmental factors. Computing analysis of these data is expected to gain knowledge of gene functions and disease mechanisms. Clustering is a classical exploratory technique of discovering similar expression patterns and function modules. However, gene expression data are usually of high dimensions and relatively small samples, which results in the main difficulty for the application of clustering algorithms. Principal component analysis (PCA) is usually used to reduce the data dimensions for further clustering analysis. While PCA estimates the similarity between expression profiles based on the Euclidean distance, which cannot reveal the nonlinear connections between genes. This paper uses nonlinear dimensionality reduction (NDR) as a preprocessing strategy for feature selection and visualization, and then applies clustering algorithms to the reduced feature spaces. In order to estimate the effectiveness of NDR for capturing biologically relevant structures, the comparative analysis between NDR and PCA is exploited to five real cancer expression datasets. Results show that NDR can perform better than PCA in visualization and clustering analysis of complex gene expression data.     

Three-dimensional data visualization and biomedical applications

Three-dimensional data visualization is an important tool in several medical, scientific, and engineering areas. Visualization methods are based on a primitive representational element: contour, surface, or volume. Methods often incorporate options to cut open, see around, or see through structures, and form images in multiple windows or with animation. To visualize and interpret two or three related 3D data sets, composite imaging methods are required. The appropriate method depends on the user needs, application area, and available hardware. Visualization of 3D medical data is described for cranium/face, musculoskeletal systems, spine, intracranial structures, cardiovascular system, and radiation therapy.     

A comparison of two boundary conditions used with the bidomain model of cardiac tissue

In the bidomain model, two alternative sets of boundary conditions at the interface between cardiac tissue and a saline bath have been used. It is shown that these boundary conditions are equivalent if the length constant of the tissue in the direction transverse to the fibers is much larger than the radius of the individual cardiac cells. If this is not the case, the relative merits of the two boundary conditions are closely related to the question of the applicability of a continuum model, such as the bidomain model, to describe a discrete multicellular tissue.     

Multimodal visualization interface for data management, self-learning and data presentation

A multimodal visualization software, called the Data Manager (DM), has been developed to increase interdisciplinary communication around the topic of visualization and modeling of various aspects of the human anatomy. Numerous tools used in Radiology are integrated in the interface that runs on standard personal computers. The available tools, combined to hierarchical data management and custom layouts, allow analyzing of medical imaging data using advanced features outside radiological premises (for example, for patient review, conference presentation or tutorial preparation). The system is free, and based on an open-source software development architecture, and therefore updates of the system for custom applications are possible.     

A novel model of the pulse decay method for measurement of local tissue blood perfusion

The blood perfusion of biological tissue is a basic parameter of physiology and medical engineering. A novel average temperature model (ATM) is presented in this paper to measure the blood perfusion of tissue based on the thermal pulse-decay method. Differing from the existing point source model (PSM) and spherical source model (SSM), the probe bead average temperature analytical solution is derived and used to estimate the blood perfusion. The blood perfusion prediction errors caused by the approximate assumptions used in each model are studied using the numerical experiment method. Contributions of the tissue parameters, probe thermistor bead parameters, and measurement parameters, such as tissue thermal conductivity, tissue thermal diffusivity, blood perfusion rate, bead thermal conductivity, bead radius, measurement time, and thermal pulse length are discussed. The predicting accuracy of the ATM model is compared with the PSM model and SSM model. The results show that, for all the cases tested, the ATM model is better than the other two.     

Advanced data analysis and visualization for clinical laboratory

This paper describes visualization techniques that help identify hidden structures in clinical laboratory data. The visualization of data is helpful for a rapid and better understanding of the characteristics of data sets. Various charts help the user identify trends in data. Scatter plots help prevent misinterpretations due to invalid data by identifying outliers. The representation of experimental data in figures is always useful for communicating results to others. Currently, flexible methods such as smoothing methods and latent structure analysis are available owing to the presence of advanced hardware and software. Principle component analysis, which is a well-known technique used to reduce multidimensional data sets, can be carried out on a personal computer. These methods could lead to advanced visualization with regard to exploratory data analysis. In this paper, we present 3 examples in order to introduce advanced data analysis. In the first example, a smoothing spline was fitted to a time-series from the control chart which is not in a state of statistical control. The trend line was clearly extracted from the daily measurements of the control samples. In the second example, principal component analysis was used to identify a new diagnostic indicator for Graves' disease. The multi-dimensional data obtained from patients were reduced to lower dimensions, and the principle components thus obtained summarized the variation in the data set. In the final example, a latent structure analysis for a Gaussian mixture model was used to draw complex density functions suitable for actual laboratory data. As a result, 5 clusters were extracted. The mixed density function of these clusters represented the data distribution graphically. The methods used in the above examples make the creation of complicated models for clinical laboratories more simple and flexible.     

Development,implementation and evaluation of an information model for archetype based user responsive medical data visualization

BackgroundWhen medical data have been successfully recorded or exchanged between systems there appear a need to present the data consistently to ensure that it is clearly understood and interpreted. A standard based user interface can provide interoperability on the visual level.ObjectivesThe goal of this research was to develop, implement and evaluate an information model for building user interfaces for archetype based medical data.MethodsThe following types of knowledge were identified as important elements and were included in the information model: medical content related attributes, data type related attributes, user-related attributes, device-related attributes. In order to support flexible and efficient user interfaces an approach that represents different types of knowledge with different models separating the medical concept from a visual concept and interface realization was chosen. We evaluated the developed approach using Guideline for Good Evaluation Practice in Health Informatics (GEP-HI).ResultsWe developed a higher level information model to complement the ISO 13606 archetype model. This enabled the specification of the presentation properties at the moment of the archetypes’ definition. The model allows realizing different users’ perspectives on the data. The approach was implemented and evaluated within a functioning EHR system. The evaluation involved 30 patients of different age and IT experience and 5 doctors. One month of testing showed that the time required reading electronic health records decreased for both doctors (from average 310 to 220 s) and patients (from average 95 to 39 s). Users reported a high level of satisfaction and motivation to use the presented data visualization approach especially in comparison with their previous experience.ConclusionThe introduced information model allows separating medical knowledge and presentation knowledge. The additional presentation layer will enrich the graphical user interface’s flexibility and will allow an optimal presentation of medical data considering the different users’ perspectives and different media used for data presentation.     

Neural network classification of otoneurological data and its visualization

When complex data is distributed in a biased manner between disease classes, classification accuracy can be increased with a network set of perceptron neural networks developed by us. A novel projection method is also introduced for the visual classification of the data to elucidate its features and disease class distribution. The set of the perceptron neural networks and the projection method were tested with otoneurological data and they improved average sensitivity and positive predictive value at least 10% up to 85% and 83%, compared to our earlier neural network classifications with the same data. The methods were also experimented with two additional data sets, which included diagnostically very difficult cases.     

A mini pig model for visualization of perforator flap by using angiography and MIMICS

Background

Research performed using animal models has assisted in the understanding of flap anatomy and physiology. Pigs’ vasculature in the skin is anatomically and physiologically similar to human, making it an ideal model for research. Until now, most vascular imaging studies are of two-dimensions. The aim of this study is to provide a three-dimensional (3D) model that reveals detailed architecture of the vascular network of the porcine, for accurate quantitative assessment.

Methods

Five Guangxi Bama minipigs were anaesthetized intramuscularly and underwent whole body lead oxide–gelatin injection. Spiral computed tomography scanning was performed on the subjects and three-dimensional reconstructions were made. Another minipig was used, and underwent Cardiografin injection. 3D-reconstruction was executed in vivo. All subjects were then dissected by layers to document the individual perforators.

Results

Angiography using perfusion with lead oxide–gelatine mixture has the advantage of illustrating distinctively the vessels and their perforating branches. However, it is incapable of displaying other tissues structures. Angiography through perfusion with Cardiografin in vivo has the advantage of demonstrating the relationship between arteries and bones. Yet it could only display coarsely the vascular trunk, and is incapable of displaying the vascular network. By combining these two methods, the 3D structure, source, course, and territories of the arteries were presented distinctively.

Conclusions

3D modeling in combination with traditional sectional imaging of the pig model enables blood vessels to be displayed more dynamically with greater realism. The procedure described could be useful for future flap research, by offering a better visualization of the vascular structure of the skin flap, allowing for better anatomical understanding.     

A rapid enzyme immunoassay for measurement of HBK in blood

An enzyme immunoassay has been developed for the measurement of HBK (4-amino-2-hydroxybutylyldibekacin) a new semisynthetic aminoglycoside antibiotic. Antisera were raised in rabbits by immunization with HBK conjugated to bovine serum albumin (BSA). 3'-Eno-HBK conjugated to alkaline phosphatase (ALP) was used as an enzyme labeled antigen. The antibody-bound drug was separated from free using goat anti-rabbit IgG serum. The assay can be completed within one hr by co-incubating the first and the second antibody. The present immunoassay allows detection of 10 ng HBK per ml of serum, and is applicable for monitoring HBK level in blood. HBK concentrations in human sera were determined by the immunoassay during and after infusion and the levels were compared with those determined by high performance liquid chromatography (HPLC) and antimicrobial assay.     

GBuilder--an application for the visualization and integration of EST cluster data

This paper presents a network-centric DNA sequence visualization and analysis tool called GBuilder. The tool is an easy-to-use Java application that can be used to analyze DNA sequence clusters and assemblies. The emphasis is on the analysis of EST data, where these highly redundant collections of low-quality and often alternatively spliced or chimeric sequence data are difficult to explore. The tool has the capacity to visualize similarities or dissimilarities between sequences at the level of the nucleotide base or annotation in many ways. Sequences may also be edited manually. The novel feature of GBuilder is its ability to access different data sources and analysis applications available on the Internet and to integrate these results and functionality back into itself. External resources such as EST cluster databases and conventional command-line analysis applications are integrated and accessed using CORBA (Common Object Request Broker Architecture), which provides a standard implementation independent protocol for integration. New CORBA services can be integrated immediately if they use a known interface described using the Interface Definition Language.