A framework for predictive modeling of anatomical deformations

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.     

Sparse decomposition and modeling of anatomical shape variation

Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.     

Probabilistic framework for brain connectivity from functional MR images

This paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data.     

Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging

A method is presented for determining paths of anatomical connection between regions of the brain using magnetic resonance diffusion tensor information. Level set theory, applied using fast marching methods, is used to generate three-dimensional time of arrival maps, from which connection paths between brain regions may be identified. The method is demonstrated in the normal brain and it is shown that major white matter tracts may be elucidated and that multiple connections and tract branching are allowed. Maps of connectivity between brain regions are also determined. Four options are described for estimating the degree of connectivity between regions.     

Joint problem of power optimal connectivity and coverage in wireless sensor networks

This work considers a multi-hop sensor network and addresses the problem of minimizing power consumption in each sensor node locally while ensuring two global (i.e., network wide) properties: (i) communication connectivity, and (ii) sensing coverage. A sensor node saves energy by suspending its sensing and communication activities according to a Markovian stochastic process. We show that a power level to induce a coverage radius is sufficient for connectivity provided that w(n)→∞. The paper presents a Markov model and its solution for steady state distributions to determine the operation of a single node. Given the steady state probabilities, we construct a non-linear optimization problem to minimize the power consumption. Simulation studies to examine the collective behavior of large number of sensor nodes produce results that are predicted by the analytical model.     

A modeling framework for supporting and evaluating connectivity in cognitive radio ad hoc networks with beamforming

In this paper, we present an analytical modeling framework for supporting and evaluating the impact of shadowing and beamforming on the topological connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) are equipped with omnidirectional antennas while secondary users (SUs) are equipped with directional antennas such as uniform linear array (ULA) antenna and uniform circular array (UCA) antenna. The main features and contributions in this paper are as follows. First, we derive a formula for calculating effective coverage area of a node in secondary network by taking the effect of path loss, antenna model, and beamforming scheme into consideration. Second, we mathematically analyze the expected number of neighbors and communication probability of a SU based on the effective coverage area of SU and the spatial–temporal existence of PU’s operation. We also derive the expression of the upper bound of path connectivity between two arbitrary SUs in the networks. Third, we point out that UCA antenna is the most suitable antenna for CRAHNs. We find the optimal number of elements corresponding to each type of directional antenna at which the highest connectivity can be achieved. The validity of our analysis is verified by comparing with simulations. The results in this paper provide efficient guidelines for system designers to characterize and optimize the connectivity of CRAHNs with beamforming.     

Multiscale modeling for image analysis of brain tumor studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlas-based segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.     

Bayesian reconstruction of functional images using anatomical information as priors

Proposes a Bayesian method whereby maximum a posteriori (MAP) estimates of functional (PET and SPECT) images may be reconstructed with the aid of prior information derived from registered anatomical MR images of the same slice. The prior information consists of significant anatomical boundaries that are likely to correspond to discontinuities in an otherwise spatially smooth radionuclide distribution. The authors' algorithm, like others proposed recently, seeks smooth solutions with occasional discontinuities; the contribution here is the inclusion of a coupling term that influences the creation of discontinuities in the vicinity of the significant anatomical boundaries. Simulations on anatomically derived mathematical phantoms are presented. Although computationally intense in its current implication, the reconstructions are improved (ROI-RMS error) relative to filtered backprojection and EM-ML reconstructions. The simulations show that the inclusion of position-dependent anatomical prior Information leads to further improvement relative to Bayesian reconstructions without the anatomical prior. The algorithm exhibits a certain degree of robustness with respect to errors in the location of anatomical boundaries.     

Joint modeling and reconstruction of a compressively-sensed set of correlated images

Employing correlation among images for improved reconstruction in compressive sensing is a conceptually attractive idea, although developing efficient modeling strategies and reconstruction algorithms are often the key to achieve any potential benefit. This paper presents a novel modeling strategy and an efficient reconstruction algorithm for processing a set of correlated images, jointly taking into consideration inter-image correlation, intra-image correlation and inter-channel correlation. The approach starts with joint modeling of the entire image set in the gradient domain, which supports simultaneous representation of local smoothness, nonlocal self-similarity of every single image, and inter-image correlation. Then an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. Furthermore, to support color image reconstruction, the proposed algorithm is extended by using the concept of group sparsity to explore inter-channel correlation. The effectiveness of the proposed approach is demonstrated with extensive experiments on both grayscale and color image sets. Results are also compared with recently proposed compressive sensing recovery algorithms.     

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust, and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological self-organization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modeling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.     

Electron crystallography for structural and functional studies of membrane proteins

Membrane proteins are important research targets for basic biological sciences and drug design, but studies of their structure and function are considered difficult to perform. Studies of membrane structures have been greatly facilitated by technological and instrumental advancements in electron microscopy together with methodological advancements in biology. Electron crystallography is especially useful in studying the structure and function of membrane proteins. Electron crystallography is now an established method of analyzing the structures of membrane proteins in lipid bilayers, which resembles their natural biological environment. To better understand the neural system function from a structural point of view, we developed the cryo-electron microscope with a helium-cooled specimen stage, which allows for analysis of the structures of membrane proteins at a resolution higher than 3 ?. This review introduces recent instrumental advances in cryo-electron microscopy and presents some examples of structure analyses of membrane proteins, such as bacteriorhodopsin, water channels and gap junction channels. This review has two objectives: first, to provide a personal historical background to describe how we came to develop the cryo-electron microscope and second, to discuss some of the technology required for the structural analysis of membrane proteins based on cryo-electron microscopy.     

Joint source and sending rate modeling in adaptive video streaming

This work addresses the modeling of traffic generated by a video source operating in the context of adaptive streaming services. Traffic modeling is a key in several network design issues, such as dimensioning of core and access network resources, developing pricing procedures, carrying out cost-revenue studies. The actual traffic generated during a video streaming session depends on both the video source and the bandwidth variations imposed by lower communication layers. We propose a new traffic model that jointly encompasses these two effects. Specifically, we consider the modeling of the sequence of frame sizes generated by a video streaming source that dynamically adapts its rate to the available communication channel bandwidth using bitstream switching techniques. In order to represent the source rate adaptation to the random network bandwidth variations on the communication channel, we resort to a framework based on Hidden Markov Processes (HMPs). Our HMP model represents the first joint source and sending rate model in adaptive streaming literature. Thanks to effective modeling assumptions on the frame size probability density function (pdf), the HMP parameters can be estimated by means of the Expectation Maximization algorithm. The traffic model is validated by numerical simulations of a mobile adaptive video streaming scenario. We study the model's ability to predict several traffic statistics, including the traffic load of a video streaming source in different network points. Besides, we evaluate the model accuracy in characterizing aggregate video traffic resulting from multiplexing various video sources. In all experiments, we show that the proposed model is able to accurately capture the traffic characteristics.     

Three-dimensional modeling for functional analysis of cardiac images: a review

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: 1) to serve as a tutorial of the field for both clinicians and technologists, 2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and 3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.     

Defect modeling studies in HgCdTe and CdTe

We have used a quasichemical formalism to calculate the native point defect densities in x = 0.22 Hg_1−xCd_xTe and CdTe. The linearized muffin-tin orbital method, based on the local density approximation and including gradient corrections, has been used to calculate the electronic contribution to the defect reaction free energies, and a valence force field model has been used to calculate the changes to the vibration free energy when a defect is created. We find the double acceptor mercury vacancy is the dominant defect, in agreement with previous interpretations of experiments. The tellurium antisite, which is a donor, is also found to be an important defect in this material. The mercury vacancy tellurium antisite pair is predicted to be well bound and is expected to be important for tellurium antisite diffusion. We consider the possibilities that the tellurium antisite is the residual donor and a Shockley-Read recombination center in HgCdTe and suggestions for further experimental work are made. We predict that the cadmium vacancy, a double acceptor, is the dominant defect for low cadmium pressures, while the cadmium interstitial, a double donor, dominates at high cadmium pressures.     

Analytical modeling for performance studies of an FLBM-basedall-optical packet switch

This letter analyzes an all-optical packet switch based on fiber loop buffer memory (FLBM). The number of recirculations of a packet in the fiber loop is limited by noise constraints whereas the total number of packets stored in the fiber loop is constrained by the number of available wavelengths. The switch operates as an output-queued switch with these constraints. We analyze this switch for its blocking performance for incoming packets     

Three-dimensional object-oriented modeling of the stomach for the purpose of microprocessor-controlled functional stimulation

Three-dimensional (3-D) object-oriented models are needed for optimizing gastric electrical stimulation by performing virtual computer experiments. The aim of the study was to create a 3-D object-oriented electromechanical model of the stomach in vivo for the purpose of microprocessor controlled functional stimulation. The stomach was modeled using coaxial truncated conoids as objects. The strength of an external stimulating electric field generated by circumferentially implanted wire electrodes is related to artificial neurogenic and myogenic control of smooth muscle depolarization and contraction. Variation of the field strength modulates the frequency and concentration of acetylcholine release, as well as the transmembrane voltage of the muscle cells. Mechanical response of the stimulated tissue was quantified by two parametric functions of the electric field strength representing the relative contractile force and geometrical displacement of the gastric surface. Data from previously conducted canine experiments were used to test the validity of the model. The model was applied to simulate contractions with different positions, orientation and number of the circumferentially implanted stimulating electrodes. The model combined most of the existing theoretical and experimental findings concerning functional gastric stimulation and can be utilized as a flexible tool for virtual medical tests involving external high-frequency (50 Hz) neural stimulation.     

Improved modeling studies of Brillouin enhanced four-wave mixing

An improved algorithm is used to integrate the full equations for Brillouin enhanced four-wave mixing (including propagation terms). The improved algorithm produces a correction of around 10% and a reduction in the computed value of the conjugate intensity compared with earlier published data     

SML-Sys: a functional framework with multiple models of computation for modeling heterogeneous system

System-on-Chip and other complex distributed hardware/software systems contain heterogeneous components. High-level modeling of such systems require frameworks that provide designers with the ability to express concepts of models of computation (MoC)s as modeling constructs. Many system-level modeling frameworks and corresponding modeling notations such as Ptolemy II and SystemC-H facilitate multi-MoC modeling but are based on imperative programming languages (C++, Java, etc). In such frameworks, the computation and communication aspects between the components of models get intertwined thereby hindering its amenability to formal analysis. In this work, we illustrate function-based semantic definitions of MoCs, which are formulated in a functional framework called SML-Sys. We illustrate through a number of examples how to create system models using this functional programming paradigm.     

Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model

Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. We make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. We also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a imagnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.     

Dynamic denoising studies in wideband radio channel measurement and modeling

The fixed level and dynamic denoising method was studied based on indoor-to-outdoor measured channel impulse responses ORs） at 5.25 GHz with radio frequency （RF） 100 MHz bandwidth. It is found that the dynamic ranges, peak powers and noise floors of the IRs are with close correlations. The comparisons with different denoising methods are given by deriving the power delay profiles （PDPs）, root mean square （RMS） delay spread （RMS DS）, number of paths （NOPs） and Ricean K-factors. It is concluded that the traditional fixed level noise cut is under estimate of DS and NOPs. The Ricean K-factors are of little sensitive to noise cut irrespective of what kind of methods applied. The PDPs are not very sensitive to the fixed level noise cut, however, obvious changes can be found by dynamic noise cut. The dynamic noise cut is preferred when clear noise floors is observed and decided from the measured IRs, it＇s of importance in data post processing for wideband radio channel measurements as well as the relevant modeling work.