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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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Imposing spatio-temporal support in magnetic resonance angiographic imaging

Imposing spatio-temporal support in magnetic resonance angio...

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conference on image reconstruction from incomplete data vI

作者： Bones, Philip J. vafadar, Bahareh Watts, Richard Wu, Bing Univ Canterbury Dept Elect & Comp Engn Computat Imaging Grp Christchurch New Zealand Univ Canterbury Dept Phys & Astron Christchurch New Zealand Duke Univ Brain Imaging & Analysis Ctr Sch Med Durham NC 27708 USA

ISBN: (纸本)9780819482969

A method to improve time resolution in 3D contrast-enhanced magnetic resonance angiography (CE-MRA) is proposed. A temporal basis based on prior knowledge of the contrast flow dynamics is applied to a sequence of image reconstructions. In CE-MRA a contrast agent (gadolinium) is injected into a peripheral vein and MR data is acquired as the agent arrives in the arteries and then the veins of the region of clinical interest. The acquisition extends over several minutes. Information is effectively measured in 3D k-space (spatial frequency space) one line at-a-time. That line may be along a Cartesian grid line in k-space, a radial line or a spiral trajectory. A complete acquisition comprises many such lines but in order to improve temporal resolution, reconstructions are made from only partial sets of k-space data. By imposing a basis for the temporal changes, based on prior expectation of the smoothness of the changes in contrast concentration with time, it is demonstrated that a significant reduction in artifacts caused by the under-sampling of k-space can be achieved. The basis is formed from a set of gamma variate functions. Results are presented for a simulated set of 2D spiral-sampled CE-MRA data.

关键词： Magnetic resonance imaging data acquisition Arteries Temporal resolution Angiography Signal to noise ratio Gadolinium image quality veins Magnetism

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Sound Field reconstruction from incomplete data by Solving Fuzzy Relational Equations

Sound Field Reconstruction from Incomplete Data by Solving F...

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International Scientific conference on Intellectual Systems of Decision-Making and Problems of Computational Intelligence (ISDMCI)

作者： Azarov, Olexiy Krupelnitskyi, Leonid Rakytyanska, Hanna Vinnytsia Natl Tech Univ Vinnytsia Ukraine

ISBN: (纸本)9783030542153;9783030542146

The approach to solving inverse problems of source identification in acoustics is proposed based on fuzzy relational calculus. The compositional rule of inference connects the real and observed fuzzy acoustic image using the relationship matrix, which reflects the degree of completeness of the microphone array measurement data. The fuzzy model of the acoustic field is based on 3D membership functions, for which the degree of membership decreases in proportion to the square of the distance to the source. The problem of reconstructing the acoustic field is formulated as the problem of inverse logical inference. The method for reconstructing the acoustic field from incomplete data is proposed based on solving fuzzy relational equations. The problem consists in finding such a number of sound sources, their locations and powers, which minimize the difference between the model and observed fuzzy acoustic image. The solutions of the equation system represent the variants of the acoustic field reconstruction in the form of the main acoustic surface and a set of secondary acoustic surfaces. The main acoustic surface is generated by the least number of sources. The set of secondary acoustic surfaces represents the variants of the sound field reconstruction generated by the upper solutions for the number of sources. Since the sources distribution is completely determined by the properties of the solution set, the proposed approach allows avoiding the generation and selection of candidate sources, that provides simplification of the reconstruction process and reduction of time costs. The genetic and neural algorithm provides accurate and fast reconstruction of the acoustic field for an unknown number of sources and their configuration.

关键词： Inverse problems in acoustics Source identification problem Sound field reconstruction Solving fuzzy relational equations

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Research of the image reconstruction from incomplete Projection data Based on the Homotopy Mapping

Research of the Image Reconstruction from Incomplete Project...

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2nd International Asia conference on Informatics in Control, Automation and Robotics (CAR)

作者： Li Fei Pan Jin-Xiao North Univ China Coll Sci Dept Math Taiyuan Peoples R China North Univ China Natl Key Lab Elect Measurement Technol Taiyuan Peoples R China

ISBN: (纸本)9781424451944

Multi-objective optimization reconstruction algorithm is one of the main methods to solve the limited angle image reconstruction. However we have great difficulty in selecting the right parameter reasonably because there is not only one objective function. So we give a new method to choose the parameter which was called homotopy parameter based on the theory of homotopy in this paper. Finally, the simulations were carried out to verify the proposed strategy, and the simulation results show that this algorithm is superior to the conventional multi-objective optimization methods in many aspects, such as less reconstruction errors, higher smoothness and gray value resolution.

关键词： multi-objective optimization limited angle image reconstruction homotopy parameter

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Removing image artifacts in optoacoustic tomography using virtual transducer restoration

Removing image artifacts in optoacoustic tomography using vi...

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conference on Photons Plus Ultrasound

作者： Patrickeyev, I Oraevsky, AA Inst Continuous Media Mech Perm Russia

ISBN: (纸本)0819452289

We propose an approach allowing significant reduction or even complete removal of artifacts that can appear in optoacoustic images acquired with limited number of transducers (missing detectors) due to incomplete data. In optoacoustic tomography the image is reconstructed from a set of acoustic transducers located on the surface of tissue irradiated by a laser. The rigorous solution of the tomographic problem requires covering of the entire surface of the illuminated volume by an array of transducers. However, in practice, only portion of the surface is available. As a result of data incompleteness, artifacts (usually looking like arc-shaped shadows extending from the bright objects) can appear. These artifacts limit the spatial resolution, degrade the image contrast and distort shapes of the reconstructed objects. The results of the numerical simulation, presented in this work, show that the intensity and the shape of the "arc-shadow" artifacts depend on the surface area of uncovered by the acoustic detectors. The cause of the artifacts appearance is the violation of the absorbed energy conservation by the image reconstruction algorithm. Such explanation of this fact represents a key for removal of these artifacts. As presented in the paper, the intensity of the artifacts could be reduced by partial restoration of the missed transducers. In case of sufficient a priori information about number of objects, the proposed algorithm can be considered as the interpolation/extrapolation of the data or substitution of the missed signal by averaged real signal taking into account energy conservation. In a common case, the signals of virtual transducers are restored from the distorted image using the solution of the wave equation. Then the cleaned image is reconstructed from the complete set of signals combining real and virtual transducers. These operations can be repeated iteratively until artifacts become weak. The accuracy of the image reconstruction depends on the number

关键词： optoacoustic imaging resolution contrast incomplete data artifacts inverse problem

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Inter-trial alignment of EEG data and phase-locking 8

Inter-trial alignment of EEG data and phase-locking

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conference on image reconstruction from incomplete data vIII

作者： Testorf, M. E. Horak, P. Connolly, A. Holmes, G. L. Jobst, B. C. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA Dartmouth Hitchcock Med Ctr Dept Neurol Lebanon NH 03766 USA Univ Vermont Coll Med Burlington VT USA

ISBN: (纸本)9781628417661

Neuro-scientific studies are often aimed at imaging brain activity, which is time-locked to external stimuli. This provides the possibility to use statistical methods to extract even weak signal components, which occur with each stimulus. For electroencephalographic recordings this concept is limited by inevitable time jitter, which cannot be controlled in all cases. Our study is based on a cross-correlation analysis of trials to alignment trials based on the recorded data. This is demonstrated both with simulated signals and with clinical EEG data, which were recorded intracranially. Special attention is given to the evaluation of the time-frequency resolved phase-locking across multiple trails.

关键词： Electroencephalogram working memory joint time-frequency transform short time Fourier transform wavelet analysis cross-correlation phase-locking

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Projection Selection for Binary Tomographic reconstruction Using Global Uncertainty 15th

Projection Selection for Binary Tomographic Reconstruction U...

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15th International conference on image Analysis and Recognition (ICIAR)

作者： Leko, Gabor Balazs, Peter varga, Laszlo G. Univ Szeged Dept Image Proc & Comp Graph Arpad Ter 2 H-6720 Szeged Hungary

ISBN: (纸本)9783319930008;9783319929996

Binary tomography focuses on the problem of reconstructing homogeneous objects from a small number of their projections. In many applications, incomplete projection data holds insufficient information for the correct reconstruction of the original object. In this paper, we provide an optimization based method to select the "most informative" projection set, using information of global uncertainty. Beside the projection data we assume no further knowledge of the image to be reconstructed. Still, we achieve approximately as accurate reconstruction results, as it is possible to gain with a former method that uses blueprint images to find the optimal set of projections. We give experimental results for validating our approach on artificial images of various structures.

关键词： Binary tomography reconstruction Uncertainty Projection selection Optimization

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Model-based Tikhonov-Miller image restoration

Model-based Tikhonov-Miller image restoration

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International conference on image Processing

作者： Barakat, v Guilpart, B Goutte, R Prost, R Commissariat a l'Energie Atomique B.P. 12 Bruyeres-Le-Chatel Bruyeres-Le-Chatel France

ISBN: (纸本)0818681837

We propose a nem Miller-Tikhonov restoration method where an a priori model of the solution is included. fn sharp contrast with fire classical method, this approach incorporates local informations. We show that the optimal model can be directly calculated from the data or a priori given and adjusted by minimizing the reconstruction error.

关键词： reconstruction error image restoration technique

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COMPARING ASTRONOMICAL dataCUBES USING LEvEL-SET TOPOLOGY

COMPARING ASTRONOMICAL DATACUBES USING LEVEL-SET TOPOLOGY

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19th IEEE International conference on image Processing (ICIP)

作者： Rosolowsky, Erik MacDonald, Braden Loeppky, Jason ASC 354-3187 Univ Way Kelowna BC V1V 1V7 Canada

ISBN: (纸本)9781467325332;9781467325349

We describe position-position-velocity data cubes derived from the star-forming interstellar medium (SFISM). The physical characteristics and evolution of the SFISM must be deduced from the incomplete information present in the observed data. Astrophysicists can simulate the evolution of the SFISM from first principles but the standard comparisons of simulated data with observations do not completely describe the SFISM. Given the hierarchical structure present in the emission, we forward the use of Reeb graphs as a tool for comparing data cubes. We describe the computation of a Reeb graph and show that the properties encoded in the Reeb graph highlight the deficiencies of simulations at capturing the structure of the SFISM.

关键词： Reeb Graphs Radio Astronomy data cubes Dendrograms

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image tag completion by low-rank factorization with dual reconstruction structure preserved

Image tag completion by low-rank factorization with dual rec...

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作者： Li, Xue Zhang, Yu-Jin Shen, Bin Liu, Bao-Di Electronic Engineering Tsinghua University Beijing100084 China Computer Science Purdue University West LafayetteIN47907 United States Information and Control Engineering China University of Petroleum Qingdao266580 China

ISBN: (纸本)9781479957514

A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factorized into a basis matrix U and a sparse coefficient matrix v, i.e. D = Uv + E. This low-rank formulation encapsulating sparse coding enables our algorithm to recover latent structures from noisy initial data and avoid performing too much denoising;2) Local reconstruction structure consistency: to steer the completion of D, the local linear reconstruction structures in feature space and tag space are obtained and preserved by U and v respectively. Such a scheme could alleviate the negative effect of distances measured by low-level features and incomplete tags. Thus, we can seek a balance between exploiting as much information and not being mislead to suboptimal performance. Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method. © 2014 IEEE.

关键词： Errors

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Optimizing image reconstruction of tissue blood flow by diffuse correlation tomography

Optimizing image reconstruction of tissue blood flow by diff...

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conference on Optical Tomography and Spectrosopy of Tissue v

作者： Zhou, C Durduran, T Yu, GQ Yodh, AG Univ Penn Dept Phys & Astron Philadelphia PA 19104 USA

ISBN: (纸本)0819447552

In this work, we analyze the image reconstruction problem within the first order Rytov approximation and optimize the image reconstruction by looking at the singular value spectrum of the "weight matrix". We show that the optimal data is from the tail of the temporal intensity autocorrelation curve by investigating the condition number. Furthermore, by doing a standard L-curve analysis, we find the optimal regularization constant (Tikhonov regularization). images from simulated data are shown to illustrate the advantage of the optimization process.

关键词： diffuse correlation spectroscopy (DCS) image reconstruction singular value analysis

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