Model-based Analysis Method Research Articles

Improved execution efficiency of model-based roentgen stereophotogrammetric analysis: simplification and segmentation of model meshes

Recently, the model-based roentgen stereophotogrammetric analysis (RSA) method has been developed as an in vivo tool to estimate static pose and dynamic motion of the instrumented prostheses. The two essential inputs for the RSA method are prosthetic models and roentgen images. During RSA calculation, the implants are often reversely scanned and input in the form of meshes to estimate the outline error between prosthetic projection and roentgen images. However, the execution efficiency of the RSA iterative calculation may limit its clinical practicability, and one reason for inefficiency may be very large number of meshes in the model. This study uses two methods of mesh manipulation to improve the execution efficiency of RSA calculation. The first is to simplify the model meshes and the other is to segment and delete the meshes of insignificant regions. An index (i.e. critical percentage) of an optimal element number is defined as the trade-off between execution efficiency and result accuracy. The predicted results are numerically validated by total knee prosthetic system. The outcome shows that the optimal strategy of the mesh manipulation is simplification and followed by segmentation. On average, the element number can even be reduced to 1% of the original models. After the mesh manipulation, the execution efficiency can be increased about 75% without compromising the accuracy of the predicted RSA results (the increment of rotation and translation error: 0.06° and 0.02 mm). In conclusion, prosthetic models should be manipulated by simplification and segmentation methods prior to the RSA calculation to increase the execution efficiency and then to improve clinical applicability of the RSA method.

Pattern Classification of Volitional Functional Magnetic Resonance Imaging Responses in Patients With Severe Brain Injury

Recent neuroimaging investigations have explored the use of mental imagery tasks as proxies for an overt motor response, in which patients are asked to imagine performing a task, such as "Imagine yourself swimming." To detect covert volitional brain activity in patients with severe brain injury using pattern classification of the blood oxygenation level-dependent (BOLD) response during mental imagery and to compare these results with those of a univariate functional magnetic resonance imaging analysis. Case-control study. Academic research. Experiments were performed in 8 healthy control subjects and in 5 patients with severe brain injury. The patients with severe brain injury constituted a convenience sample. Functional magnetic resonance imaging data were acquired as the patients were asked to follow commands or to answer questions using motor imagery as a proxy response. In the controls, the responses were accurately classified. In the patient group, the responses of 3 of 5 patients were correctly classified. The remaining 2 patients showed no significant BOLD response in a standard univariate analysis, suggesting that they did not perform the task. In addition, we showed that a classifier trained on command-following data can be used to evaluate a later communication run. This technique was used to successfully disambiguate 2 potential BOLD responses to a single question. Pattern classification in functional magnetic resonance imaging is a promising technique for advancing the understanding of volitional brain responses in patients with severe brain injury and may serve as a powerful complement to traditional general linear model-based univariate analysis methods.

Image Analysis and Modeling in Medical Image Computing

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

Using a symbolic process model as input for model-based fMRI analysis: Locating the neural correlates of problem state replacements

In this paper, a model-based analysis method for fMRI is used with a high-level symbolic process model. Participants performed a triple-task in which intermediate task information needs to be updated frequently. Previous work has shown that the associated resource - the problem state resource - acts as a bottleneck in multitasking. The model-based method was used to locate the neural correlates of 'problem state replacements'. To analyze the fMRI data, we fit the computational process model to the behavioral data and regressed the model's activity against the fMRI data. The brain region responsible for the temporary representation of problem states, the inferior parietal lobule, and the brain region responsible for long-term storage of problem states, the inferior frontal gyrus were thus identified. These results show that model-based fMRI analyses can be performed using high-level symbolic cognitive models, enabling fine-grained exploratory fMRI research.

A calorimetric study of carbamate formation

Post combustion capture of CO 2 (PCC) is currently one of the leading technologies for the reduction of green house gas emissions from power plants. The most common PCC process is based on the absorption of CO 2 into aqueous amine solutions. CO 2 absorption involves several parallel reactions including hydration of CO 2; deprotonation of carbonic acid; protonation of the amine; and formation of carbamate. The extent to which each reaction proceeds is dependent on the conditions of absorption (or desorption), as well as the associated equilibrium constants. All reactions other than carbamate formation have been extensively investigated previously. The investigation of carbamate formation is more complex, as it cannot be studied in the absence of other, simultaneous reactions. In particular, the enthalpy of carbamate formation has been determined previously only from the temperature dependence of equilibrium constants, but this methodology is not robust. In this contribution, we use calorimetry and advanced model-based data analysis methods for the unravelling of the thermo-chemistry relevant to PCC and specifically directly determine the reaction enthalpy for carbamate formation. The reaction enthalpies of carbamate formation and amine protonation were measured in dilute aqueous solution at 298 K using isothermal titration calorimetry (ITC) for monoethanolamine (MEA), diethanolamine (DEA), and ammonia (NH 3). The enthalpy of protonation was also measured for carbonate and bicarbonate. The re-determined protonation constants of the carbonate species and the three amines are in excellent agreement with previously reported results. No measured enthalpies of carbamate formation have been reported previously. For the carbamate formation reaction HCO 3 - + R ′ RNH ↔ R ′ RNCO 2 - , the following reaction enthalpies were determined: MEA, −29.7 ± 0.1 kJ/mol; DEA, −23.7 ± 0.9 kJ/mol, and NH 3, −27.6 ± 0.9 kJ/mol. The results are in good agreement with, but much more precise and robust than estimated values reported in the literature.

Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications

Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins.

Contrasting identity-by-descent estimators, association studies, and linkage analyses using the Framingham Heart Study data

We explored the utility of population- and pedigree-based analyses using the Framingham Heart Study genome-wide 50 k single-nucleotide polymorphism marker data provided for Genetic Analysis Workshop 16. Our aims were: 1) to compare identity-by-descent sharing estimates from variable amounts of data; 2) to apply each of these estimates to a case-control association study designed to control for relatedness among samples; and 3) to contrast these results to those obtained using model-based and model-free linkage analysis methods.

A model-based analysis method for detection of single-base substitution using resequencing microarrays

A model-based analysis method for detection of single-base substitution using resequencing microarrays

Detecting Bat Calls: An Analysis of Automated Methods

Long-term and large-scale acoustic surveys of bats have become possible with the increased availability of recording hardware and advances in battery and memory storage technologies. The volume of data generated in surveys necessitates automated call detection, either in real time via a triggering function or offline, yet researchers are hesitant to replace traditional hand analysis without a thorough understanding of the accuracy and costs of automated detection. We compared detection accuracy and computational cost of the underlying algorithms used in commercial detectors (a zero-crossing detector, a spectral peak detector, and a high-band energy detector) with a model-based analysis method called the links detector. We predicted that the links detector would be more accurate than the other detectors, producing a larger effective detection range, because the links detector uses more information to make detection decisions. We also predicted that the links detector would be the most computationally expensive algorithm because of the processing needed for the extra information. We quantified the performance of the detectors using a synthetic recording environment, which provided an absolute ground truth for the experiments and allowed us to measure the effective detection range of each algorithm. The zero-crossing and high-band energy detectors, the fastest, were about 40 times faster than the links detector. Most of the computational cost was attributed to the filter used to remove low-frequency noise. The links detector, the most accurate, increased effective detection range by 6–12 m compared to the other detectors depending on species. The results will allow bat researchers to better understand the costs and benefits of automated detection methods.

Model-based quantitative laser Doppler flowmetry in skin

Laser Doppler flowmetry (LDF) can be used for assessing the microcirculatory perfusion. However, conventional LDF (cLDF) gives only a relative perfusion estimate for an unknown measurement volume, with no information about the blood flow speed distribution. To overcome these limitations, a model-based analysis method for quantitative LDF (qLDF) is proposed. The method uses inverse Monte Carlo technique with an adaptive three-layer skin model. By analyzing the optimal model where measured and simulated LDF spectra detected at two different source-detector separations match, the absolute microcirculatory perfusion for a specified speed region in a predefined volume is determined. qLDF displayed errors<12% when evaluated using simulations of physiologically relevant variations in the layer structure, in the optical properties of static tissue, and in blood absorption. Inhomogeneous models containing small blood vessels, hair, and sweat glands displayed errors<5%. Evaluation models containing single larger blood vessels displayed significant errors but could be dismissed by residual analysis. In vivo measurements using local heat provocation displayed a higher perfusion increase with qLDF than cLDF, due to nonlinear effects in the latter. The qLDF showed that the perfusion increase occurred due to an increased amount of red blood cells with a speed>1 mm∕s.

Statistical Analysis Methods For QT/QTc Prolongation

Prolonged QT interval is associated with life-threatening arrhythmias. Regulatory authorities have paid special attention to investigating the drug-induced delay of cardiac repolarization. Studies aimed at evaluating QT prolongation have become a routine part of safety packages across the pharmaceutical industry. However, the assessment of QT interval prolongation on the surface electrocardiogram is complicated by the fact that many factors influence the duration of the QT interval, among which heart rate plays a predominant role. Some widely used corrections of the QT interval for varying heart rate are known to be inadequate. Many alternatives have been proposed in the literature. Using information obtained from Eli Lilly thorough QT studies, we examine the performance of several approaches to the analysis of QT changes, including subject-specific (individual) QT corrections and model-based QT analysis methods. The simulation results indicate that the mixed-effects modeling approach proposed in this paper is more powerful than the other methods, all of which are commonly used in QT studies.

Mechanistic kinetic model for symmetric carboligations using benzaldehyde lyase

For reactions using thiamine diphosphate (ThDP)-dependent enzymes many empirically-derived kinetic models exist. However, there is a lack of mechanistic kinetic models. This is especially true for the synthesis of symmetric 2-hydroxy ketones from two identical aldehydes, with one substrate acting as the donor and the other as the acceptor. In this contribution, a systematic approach for deriving such a kinetic model for thiamine diphosphate (ThDP)-dependent enzymes is presented. The derived mechanistic kinetic model takes this donor-acceptor principle into account by containing two K(m)-values even for identical substrate molecules. As example the stereoselective carbon-carbon coupling of two 3,5-dimethoxy-benzaldehyde molecules to (R)-3,3',5,5'-tetramethoxy-benzoin using benzaldehyde lyase (EC 4.1.2.38) from Pseudomonas fluorescens is studied. The model is derived using a model-based experimental analysis method which includes parameter estimation, model analysis, optimal experimental design, in silico experiments, sensitivity analysis and model revision. It is shown that this approach leads to a robust kinetic model with accurate parameter estimates and an excellent prediction capability.

A model-based method for flow limitation analysis in the heterogeneous human lung

Flow limitation in the airways is a fundamental process constituting the maximal expiratory flow-volume curve. Its location is referred to as the choke point. In this work, expressions enabling the calculation of critical flows in the case of wave-speed, turbulent or viscous limitation were derived. Then a computational model for the forced expiration from the heterogeneous lung was used to analyse the regime and degree of flow limitation as well as movement and arrangement of the choke points. The conclusion is that flow limitation begins at similar time in every branch of the bronchial tree developing a parallel arrangement of the choke points. A serial configuration of flow-limiting sites is possible for short time periods in the case of increased airway heterogeneity. The most probable locations of choke points are the regions of airway junctions. The wave-speed mechanism is responsible for flow choking over most of vital capacity and viscous dissipation of pressure for the last part of the test. Turbulent dissipation, however, may play a significant role as a supporting factor in transition between wave-speed and viscous flow limitation.

Model-based security analysis in seven steps — a guided tour to the CORAS method

This paper presents the CORAS method for model-based security analysis. The presentation is case-driven. We follow two analysts in their interaction with an organisation by which they have been hired to carry out a security risk analysis. The analysis is divided into seven main steps, and the paper devotes a separate section to each of them. The paper focuses in particular on the use of the CORAS security risk modelling language as a means for communication and interaction during the seven steps.

MDABench: Customized benchmark generation using MDA

This paper describes an approach for generating customized benchmark suites from a software architecture description following a Model Driven Architecture (MDA) approach. The benchmark generation and performance data capture tool implementation (MDABench) is based on widely used open source MDA frameworks. The benchmark application is modeled in UML and generated by taking advantage of the existing community-maintained code generation “cartridges” so that current component technology can be exploited. We have also tailored the UML 2.0 Testing Profile so architects can model the performance testing and data collection architecture in a standards compatible way. We then extended the MDA framework to generate a load testing suite and automatic performance measurement infrastructure. This greatly reduces the effort and expertise needed for benchmarking with complex component and Web service technologies while being fully MDA standard compatible. The approach complements current model-based performance prediction and analysis methods by generating the benchmark application from the same application architecture that the performance models are derived from. We illustrate the approach using two case studies based on Enterprise JavaBean component technology and Web services.

Variability of the BOLD response over time: An examination of within-session differences

Model-based analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) in reaction to experimental stimuli or events. This knowledge is incorporated into the hemodynamic response function (HRF), which is a common model of the HR. Although it is already known that the HR varies across individuals and brain regions, few studies have investigated how variations within one session affect the results of statistical analysis using the general linear model (GLM). In this study, we formally tested for a possible variation of the BOLD response during prolonged functional measurement (120 min). To provoke performance of simple visual, motor, and cognitive tasks, we opted for a combination of a variant of the Stroop task and rotating L’s. In selected regions of interest, time courses were extracted and compared with regard to mean and maximum amplitudes throughout the time of functional measurement. Additionally, parameter estimates derived from the GLM were tested for differences over time. Although differences between conditions were found to be significant, results did not show significant variance due to a within-factor time. Similarly, a temporal change in the relation between conditions, in terms of an interaction between the within-factor time and the within-factor condition, was not detectable by a repeated measures ANOVA. Similar results were obtained for analysis of mean and maximum amplitudes as well as for the analyses of parameter estimates.

Versatile Formulation for Multiobjective Reliability-Based Design Optimization

This paper develops a multiobjective optimization methodology for system design under uncertainty. Model-based reliability analysis methods are used to compute the probabilities of unsatisfactory performance at both component and system levels. Combined with several multiobjective optimization formulations, a versatile reliability-based design optimization (RBDO) approach is used to achieve a tradeoff between two objectives and to generate the Pareto frontier for decision making. The proposed RBDO approach uses direct reliability analysis to decouple the reliability and optimization iterations, instead of inverse first-order reliability analysis as other existing decoupled approaches. This helps to solve a wide variety of RBDO problems with competing objectives, especially when system-level reliability constraints need to be considered. The approach also allows more accurate methods to be used for reliability analysis, and reliability terms to be included in the objective function. Two important automotive quality objectives, related to the door closing effort (evaluated using component reliability analysis) and the wind noise (evaluated using system reliability analysis), are used to illustrate the proposed method.

Model-based source localization of extracellular action potentials

A new model-based analysis method was set up for revealing information encrypted in extracellular spatial potential patterns of neocortical action potentials. Spikes were measured by extracellular linear multiple microelectrode in vivo cat’s primary auditory cortex and were analyzed based on current source density (CSD) distribution models. Validity of the monopole and other point source approximations were tested on the measured potential patterns by numerical fitting. We have found, that point source models could not provide accurate description of the measured patterns. We introduced a new model of the CSD distribution on a spiking cell, called counter-current model (CCM). This new model was shown to provide better description of the spatial current distribution of the cell during the initial negative deflection of the extracellular action potential, from the onset of the spike to the negative peak. The new model was tested on simulated extracellular potentials. We proved numerically, that all the parameters of the model could be determined accurately based on measurements. Thus, fitting of the CCM allowed extraction of these parameters from the measurements. Due to model fitting, CSD could be calculated with much higher accuracy as done with the traditional method because distance dependence of the spatial potential patterns was explicitly taken into consideration in our method. Average CSD distribution of the neocortical action potentials was calculated and spatial decay constant of the dendritic trees was determined by applying our new method.

A Real-Time Model-Based Human Motion Tracking and Analysis for Human-Computer Interface Systems

This paper introduces a real-time model-based human motion tracking and analysis method for human computer interface (HCI). This method tracks and analyzes the human motion from two orthogonal views without using any markers. The motion parameters are estimated by pattern matching between the extracted human silhouette and the human model. First, the human silhouette is extracted and then the body definition parameters (BDPs) can be obtained. Second, the body animation parameters (BAPs) are estimated by a hierarchical tritree overlapping searching algorithm. To verify the performance of our method, we demonstrate different human posture sequences and use hidden Markov model (HMM) for posture recognition testing.

Cognitive Task Analysis and Performance Modelling for Early Human Systems Integration

Successful human systems integration demands early involvement of human factors professionals, using a range of model-based analysis and assessment methods. Typically these rely on task analysis data supported by various algorithms that provide measures of performance quality. This paper reports on a validation study with one such method, for cognitive task analysis of skilled operator performance, task performance time estimation, workload assessment, and human error analysis. This was used to model train drivers' response to route features and derive performance time and workload estimates. The modelled scenarios were run in a cab simulator with N=17 driver subjects. Observational data were used to compare their strategy, performance times and workload profiles against predictions. The results show that the method produces data that are a useful approximation of real performance strategy, timings and workload.