12209134
JOINT ESTIMATION OF IMAGE AND COIL SENSITIVITIES IN PARALLEL SPIRAL MRI
Spiral MRI has received increasing attention due to its reduced T 2*-decay and robustness against bulk physiologic motion. In parallel imaging, spiral trajectories are especially of great interest due to their inherent self-calibration capabilities, which is especially useful for dynamic imaging applications such as fMRI and cardiac imaging. The existing self-calibration techniques for spiral use the k-space center data that are sampled densely in the accelerated acquisition for coil sensitivity estimation. There exists a trade-off in choosing the radius of the center data: it must be sufficiently large to contain all major spatial frequencies of coil sensitivity, but not too large to cause significant aliasing artifacts due to undersampling below Nyquist rate as the trajectory moves away from the center k-space. To address this tradeoff, we generalize the JSENSE approach, which has demonstrated success in Cartesian case, to spiral trajectory. Specifically, the method jointly estimates the coil sensitivities and reconstructs the desired image through cross validations so that the sensitivities are estimated from the full data recovered by SENSE instead of the center k-space data only, thereby increasing high frequency information without introducing aliasing artifacts. We use experimental results to show the proposed method improves sensitivities, which leads to a more accurate SENSE reconstruction
14675051
REGULARIZED SENSE RECONSTRUCTION USING ITERATIVELY REFINED TOTAL VARIATION METHOD
SENSE has been widely accepted as one of the standard reconstruction algorithms for parallel MRI. When large acceleration factors are employed, the SENSE reconstruction becomes very ill-conditioned. For Cartesian SENSE, Tikhonov regularization has been commonly used. However, the Tikhonov regularized image usually tends to be overly smooth, and a high-quality regularization image is desirable to alleviate this problem but is not available. In this paper, we propose a new SENSE regularization technique that is based on total variation with iterated refinement using Bregman iteration. It penalizes highly oscillatory noise but allows sharp edges in reconstruction without the need for prior information. In addition, the Bregman iteration refines the image details iteratively. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction
1710451
Joint estimation of image and coil sensitivities in parallel MRI
Parallel magnetic resonance imaging (MRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various dynamic imaging applications. However, there are still a number of image reconstruction issues that have not been fully addressed, thereby limiting the level of speed enhancement achievable with the technology. This paper considers the inaccuracy of coil sensitivities in conventional reconstruction methods such as SENSE, and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative algorithm. Experimental results demonstrate the effectiveness of the proposed method especially when large acceleration factors are used
10223976
A combinational feature selection and ensemble neural network method for classification of gene expression data
BackgroundMicroarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification.ResultsWe validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets.ConclusionsThus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.
12487339
Mining community structure of named entities from free text
Although community discovery has been studied extensively in the Web environment, limited research has been done in the case of free text. Co-occurrence of words and entities in sentences and documents usually implies connections among them. In this paper, we investigate the co-occurrences of named entities in text, and mine communities among these entities. We show that identifying communities from free text can be transformed into a graph clustering problem. A hierarchical clustering algorithm is then proposed. Our experiment shows that the algorithm is effective to discover named entity communities from text documents.
11383614
Mining data records in Web pages
A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.
14202528
Adding the temporal dimension to search - a case study in publication search
The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper, we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers are ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.
31577892
Demand flow network
This paper had brought up the conceptual model of demand flow network (DFN) on the basis of supply chains theories. The DFN model, DFN value model and DFN core competency model had also been established and analyzed separately in accordance with related theory. The application of DFN theory also had been studied