Progressive Magnetic Resonance Image Reconstruction Based on Iterative Solution of a Sparse Linear System

Image reconstruction from nonuniformly sampled spatial frequency domain data is an important problem that arises in computed imaging. Current reconstruction techniques suffer from limitations in their model and implementation. In this paper, we present a new reconstruction method that is based on solving a system of linear equations using an efficient iterative approach. Image pixel intensities are related to the measured frequency domain data through a set of linear equations. Although the system matrix is too dense and large to solve by direct inversion in practice, a simple orthogonal transformation to the rows of this matrix is applied to convert the matrix into a sparse one up to a certain chosen level of energy preservation. The transformed system is subsequently solved using the conjugate gradient method. This method is applied to reconstruct images of a numerical phantom as well as magnetic resonance images from experimental spiral imaging data. The results support the theory and demonstrate that the computational load of this method is similar to that of standard gridding, illustrating its practical utility

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

A New Solution to the Gridding Problem

Image reconstruction from nonuniformly sampled frequency domain data is an important problem that arises in computed imaging. The current reconstruction techniques suffer from fundamental limitations in their model and implementation that result in blurred reconstruction and/or artifacts. Here, we present a new approach for solving this problem that relies on a more realistic model and involves an explicit measure for the reconstruction accuracy that is optimized iteratively. The image is assumed piecewise constant to impose practical display constraints using pixels. We express the mapping of these unknown pixel values to the available frequency domain values as a linear system. Even though the system matrix is shown to be dense and too large to solve for practical purposes, we observe that applying a simple orthogonal transformation to the rows of this matrix converts the matrix into a sparse format. The transformed system is subsequently solved using the conjugate gradient method. The proposed method is applied to reconstruct images of a numerical phantom as well as actual magnetic resonance images using spiral sampling. The results support the theory and show that the computational load of this method is similar to that of other techniques. This suggests its potential for practical use

Ultrasound elastography as a motion estimation problem

Abstract—We study the feasibility of processing B-mode images instead of RF data to generate ultrasound elastography displacement fields. We use the exhaustive search (ES) algorithm which is a basic block matching algorithm from the video compression domain. We also apply two modifications to ES that can enhance both speed and detectability. Quantitative measurements of accuracy and runtime are presented. Results and potential future work are discussed. I

Multi-component fiber track modelling of diffusion-weighted magnetic resonance imaging data

In conventional diffusion tensor imaging (DTI) based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. Even though this assumption can be valid in some cases, the general case involves the mixing of components, resulting in significant deviation from the single tensor model. Hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of DTI. This project aims to work towards the development and experimental verification of a robust method for solving the problem of multi-component modelling of diffusion tensor imaging data. The new method demonstrates significant error reduction from the single-component model while maintaining practicality for clinical applications, obtaining more accurate Fiber tracking results

Image reconstruction using shift-variant resampling kernel for

magnetic resonance imagin

BicATPlus: An automatic comparative tool for Bi/Clustering of gene expression data obtained using microarrays

In the last few years the gene expression microarray technology has become a central tool in the field of functional genomics in which the expression levels of thousands of genes in a biological sample are determined in a single experiment. Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes into subsets that share biological significance. However, it is not clear how the different methods compare with each other with respect to the biological relevance of the biclusters and clusters as well as with other characteristics such as robustness and predictability. This research describes the development of an automatic comparative tool called BicATplus that was designed to help researchers in evaluating the results of different bi/clustering methods, compare the results against each others and allow viewing the comparison results via convenient graphical displays. BicAT plus incorporates a reasonable biological comparative methodology based on the enrichment of the output bi/clusters with gene ontology functional categories. No exact algorithm can be considered the optimum one. Instead, bi/clustering algorithms can be used as integrated techniques to highlight the most enriched biclusters that help biologists to draw biological prediction about the unknown genes