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PubMed2003

A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction.

We evaluate a wavelet-based algorithm to estimate the coil sensitivity modulation from surface coils. This information is used to improve the image homogeneity of magnetic resonance imaging when a surface coil is used for reception, and to increase image encoding speed by reconstructing images from under-sampled (aliased) acquisitions using parallel magnetic resonance imaging (MRI) methods for higher spatiotemporal image resolutions. The proposed algorithm estimates the spatial sensitivity profile of surface coils from the original anatomical images directly without using the body coil for additional reference scans or using coil position markers for electromagnetic model-based calculations. No prior knowledge about the anatomy is required for the application of the algorithm. The estimation of the coil sensitivity profile based on the wavelet transform of the original image data was found to provide a robust method for removing the slowly varying spatial sensitivity pattern of the surface coil image and recovering full FOV images from two-fold acceleration in 8-channel parallel MRI. The results, using bi-orthogonal Daubechies 97 wavelets and other members in this family, are evaluated for T1-weighted and T2-weighted brain imaging.

Algorithms↗

PubMed1998

Filter-based coded-excitation system for high-speed ultrasonic imaging.

We have recently presented a new algorithm for high-speed parallel processing of ultrasound pulse-echo data for real-time three-dimensional (3-D) imaging. The approach utilizes a discretized linear model of the echo data received from the region of interest (ROI) using a conventional beam former. The transmitter array elements are fed with binary codes designed to produce distinct impulse responses from different directions in ROI. Image reconstruction in ROI is achieved with a regularized pseudoinverse operator derived from the linear receive signal model. The reconstruction operator can be implemented using a transversal filter bank with every filter in the bank designed to extract echoes from a specific direction in the ROI. The number of filters in the bank determines the number of image lines acquired simultaneously. In this paper, we present images of a cyst phantom reconstructed based on our formulation. A number of issues of practical significance in image reconstruction are addressed. Specifically, an augmented model is introduced to account for imperfect blocking of echoes from outside the ROI. We have also introduced a column-weighting algorithm for minimizing the number of filter coefficients. In addition, a detailed illustration of a full image reconstruction using subimage acquisition and compounding is given. Experimental results have shown that the new approach is valid for phased-array pulse-echo imaging of speckle-generating phantoms typically used in characterizing medical imaging systems. Such coded-excitation-based image reconstruction from speckle-generating phantoms, to the best of our knowledge, have not been reported previously.

Artifacts↗

PubMed1996

Multispectral magnetic resonance images segmentation using fuzzy Hopfield neural network.

This paper demonstrates a fuzzy Hopfield neural network for segmenting multispectral MR brain images. The proposed approach is a new unsupervised 2-D Hopfield neural network based upon the fuzzy clustering technique. Its implementation consists of the combination of 2-D Hopfield neural network and fuzzy c-means clustering algorithm in order to make parallel implementation for segmenting multispectral MR brain images feasible. For generating feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need for finding weighting factors in the energy function which is formulated and based on a basic concept commonly used in pattern classification, called the 'within-class scatter matrix' principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The experimental results show that a near optimal solution can be obtained using the fuzzy Hopfield neural network based on the within-class scatter matrix.

Algorithms↗

PubMed2000

A three-dimensional ray-driven attenuation, scatter and geometric response correction technique for SPECT in inhomogeneous media.

The qualitative and quantitative accuracy of SPECT images is degraded by physical factors of attenuation, Compton scatter and spatially varying collimator geometric response. This paper presents a 3D ray-tracing technique for modelling attenuation, scatter and geometric response for SPECT imaging in an inhomogeneous attenuating medium. The model is incorporated into a three-dimensional projector-backprojector and used with the maximum-likelihood expectation-maximization algorithm for reconstruction of parallel-beam data. A transmission map is used to define the inhomogeneous attenuating and scattering object being imaged. The attenuation map defines the probability of photon attenuation between the source and the scattering site, the scattering angle at the scattering site and the probability of attenuation of the scattered photon between the scattering site and the detector. The probability of a photon being scattered through a given angle and being detected in the emission energy window is approximated using a Gaussian function. The parameters of this Gaussian function are determined using physical measurements of parallel-beam scatter line spread functions from a non-uniformly attenuating phantom. The 3D ray-tracing scatter projector-backprojector produces the scatter and primary components. Then, a 3D ray-tracing projector-backprojector is used to model the geometric response of the collimator. From Monte Carlo and physical phantom experiments, it is shown that the best results are obtained by simultaneously correcting attenuation, scatter and geometric response, compared with results obtained with only one or two of the three corrections. It is also shown that a 3D scatter model is more accurate than a 2D model. A transmission map is useful for obtaining measurements of attenuation and scatter in SPECT data, which can be used together with a model of the geometric response of the collimator to obtain corrected images with quantitative and diagnostically accurate information.

Algorithms↗

PubMed2000

Power of heterogeneous computing as a vehicle for implementing E(3) medical decision support systems.

A computer system of PCs, workstations, minicomputers etc. connected together via a local area network or wide area network represents a large pool of computational power. Our aim is to use this power for the implementation of an E(3) (efficiency, effectiveness, efficacy) medical decision support system, which can be based on different models, the best providing an explanation together with an accurate and reliable response. One of the most viable among models is decision trees, already used for many medical decision-making purposes. In this paper, we present a parallel implementation of a genetic algorithm on a heterogeneous computing system for the induction of decision trees with the application on solving the mitral valve prolapse syndrome. Our approach can be considered as a good choice for different real-world decision making, with respect to the advantages of our model, especially the great computational power.

Algorithms↗

PubMedDate not supplied

Data visualization during the early stages of drug discovery.

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software.

Drug Design↗

PubMed1998

Incremental beamwise backprojection using geometrical symmetries for 3D PET reconstruction in a cylindrical scanner geometry.

Cho et al have proposed a fast backprojection scheme for parallel beam geometries, the incremental algorithm, which performs backprojection on a ray-by-ray (beam-by-beam) basis as opposed to a pixel-by-pixel approach. We present an extension of this incremental, beamwise, backprojection algorithm to the case of volume reconstruction of data acquired by a cylindrical, multiring positron tomograph. Use is made of geometrical symmetries of the image volume. This method results in a twelve-fold reduction of execution time compared with a straightforward, voxel-driven implementation of the same interpolation equations.

Algorithms↗

PubMed2006

Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction.

Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method.

Algorithms↗

PubMed2000

PRoMT: inferring demographic history from DNA sequences.

UNLABELLED: I describe a parallel implementation of Rogers' mismatch algorithm, a method for making inferences about demographic history from DNA sequence data. The program is distributed on clusters of workstations, providing a substantial speedup and low execution times on large numbers of nodes. AVAILABILITY: Source code and documentation are available at http://mombasa.anthro.utah.edu/wooding/ CONTACT: stephen.wooding@anthro.utah.edu

Algorithms↗

PubMed2002

New developments in the theory and interpretation of X-ray spectra based on fast parallel calculations.

There has been dramatic progress over the past decade both in theory and in ab initio calculations of X-ray absorption fine structure. Significant progress has also been made in understanding X-ray absorption near-edge structure (XANES). This contribution briefly reviews the developments in this field leading up to the current state. One of the key advances has been the development of several ab initio codes such as FEFF, which permit an interpretation of the spectra in terms of geometrical and electronic properties of a material. Despite this progress, XANES calculations have remained challenging both to compute and to interpret. However, recent advances based on parallel Lanczos multiple-scattering algorithms have led to speed increases of typically two orders of magnitude, making fast calculations practicable. Improvements in the interpretation of near-edge structure have also been made. It is suggested that these developments can be advantageous in structural biology, e.g. in post-genomics studies of metalloproteins.

Absorptiometry, Photon↗

PubMed2006

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.

Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.

Algorithms↗

PubMed2006

Distributed support vector machines.

A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes). In several examples, it has been found that a reasonably small amount of information is interchanged among nodes to obtain an SVM solution, which is better than that obtained when classifiers are trained only with the local data and comparable (although a little bit worse) to that of the centralized approach (obtained when all the training data are available at the same place). We propose and analyze two distributed schemes: a "naïve" distributed chunking approach, where raw data (support vectors) are communicated, and the more elaborated distributed semiparametric SVM, which aims at further reducing the total amount of information passed between nodes while providing a privacy-preserving mechanism for information sharing. We show the feasibility of our proposal by evaluating the performance of the algorithms in benchmarks with both synthetic and real-world datasets.

Algorithms↗

PubMed2006

An improved neural network model for the two-page crossing number problem.

The simplest graph drawing method is that of putting the vertices of a graph on a line and drawing the edges as half-circles either above or below the line. Such drawings are called two-page book drawings. The smallest number of crossings over all two-page drawings of a graph G is called the two-page crossing number of G. Cimikowski and Shope have solved the two-page crossing number problem for an n-vertex and m-edge graph by using a Hopfield network with 2 m neurons. We present here an improved Hopfield model with m neurons. The new model achieves much better performance in the quality of solutions and is more efficient than the model of Cimikowski and Shope for all graphs tested. The parallel time complexity of the algorithm, without considering the crossing number calculations, is O(m) for the new Hopfield model with m processors clearly outperforming the previous algorithm.

Algorithms↗

PubMed1994

Optimization dynamics for partitioned neural networks.

Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.

Kinetics↗

PubMed1997

The convergence of backpropagation trained neural networks for various weight update frequencies.

One of the problems concerning the backpropagation training of feed-forward neural networks is the effect of the weight update frequency. This aspect influences the efficiency of parallel implementations of the training algorithm where the training vectors are distributed among processors. In this paper the convergence of two applications for various weight update intervals is reported. Further, several models are proposed for describing convergence and learning rate aspects in the context of a set of weight update intervals. The results show that the convergence by updating the weights after each training vector leads to about 10 times less number of training iterations compared to updating the weights only ones for the whole training set.

Algorithms↗

PubMed2006

Modeling oligonucleotide microarray signals.

Chemical principles dictate that the specific binding of a target to its complementary probes on a DNA microarray surface, and the nonspecific binding between other nucleotide segments and the same probes, are mutually competitive. We demonstrate that this mechanism can be understood by considering the competitive chemical reaction taking place on the microarray surface. Inspired by the pioneering work of Zhang and Hekstra, we have developed a physical model for microarray signal analysis, based on possible reaction mechanisms, and implemented it with a parallel, generic, simulated-annealing algorithm. Using data supplied by the Affymetrix Latin-square spike-in experiments, our model showed excellent fitting of the data. This correlation between the predicted expression levels and the spike-in concentrations of test transcripts demonstrated good predictive abilities of our model.

Algorithms↗

PubMed2005

Robust nonrigid registration to capture brain shift from intraoperative MRI.

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.

Algorithms↗

PubMed2004

Fast tree search for a triangular lattice model of protein folding.

Using a triangular lattice model to study the designability of protein folding, we overcame the parity problem of previous cubic lattice model and enumerated all the sequences and compact structures on a simple two-dimensional triangular lattice model of size 4+5+6+5+4. We used two types of amino acids, hydrophobic and polar, to make up the sequences, and achieved 2(23)+2(12) different sequences excluding the reverse symmetry sequences. The total string number of distinct compact structures was 219,093, excluding reflection symmetry in the self-avoiding path of length 24 triangular lattice model. Based on this model, we applied a fast search algorithm by constructing a cluster tree. The algorithm decreased the computation by computing the objective energy of non-leaf nodes. The parallel experiments proved that the fast tree search algorithm yielded an exponential speed-up in the model of size 4+5+6+5+4. Designability analysis was performed to understand the search result.

Algorithms↗