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

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Development of a dual-head mobile DOI-TOF PET system having multi- modality compatibility

Development of a dual-head mobile DOI-TOF PET system having ...

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IEEE Nuclear Science Symposium / Medical Imaging conference (NSS/MIC)

作者： Yoshiyuki, Yamakawa Kobayashi, Tetsuya Furuta, Masafumi Sato, Masanobu Ohi, Junichi Tonami, Hiromichi Furumiya, Tetsuo Tsuda, Tomoaki Nakazawa, Masayuki Hashizume, Nobuya Kitamura, Keishi Shimadzu Co Ltd Technol Res Lab Kyoto 6190237 Japan

ISBN: (纸本)9781479960972

We previously proposed the concept of FlexiblePET, a dual-head mobile DOI-TOF PET system which scans the patient lying on a bed equipped by another imaging/therapy device. Following the development of a small prototype with dual-head SiPM-based detectors showing a proof-of-concept for MR compatibility, we are now developing a human prototype with DOI-TOF detectors and a scalable data acquisition system. Each detector module consists of a 2-D crystal array (2.9 mm x 2.9 mm x 20 mm LGSO in a 16 x 16 array), a light guide and a 4 x 4 4-ch SiPM array. The detector has a four-layer DOI capability by a special reflector arrangement and is expected to have < 500 ps coincidence timing resolution. The scanner has two arced detector heads (central angle: 135 degree, diameter: 778 mm), and each consists of 18 detector modules in transaxial direction and 3 rings in axial direction. This geometric configuration provides 715 mm transaxial FOV and 150 mm axial FOV. The detector head arrangement is changeable into four types: Top-Bottom, Left-Right, Side-C and Top-C, depending on imaging purpose. In addition, high-sensitivity imaging is possible by moving detector heads close to the patient. To compensate image quality degradation caused by the limited angle coincidence measurement, which is inherent in stable dualhead scanning, a new regularized TOF list-mode reconstruction algorithm that combines weighted maximum likelihood estimation and projection-space regularization was also developed. In this study, we will report initial results of physical performance evaluations of the prototype FlexiblePET system according to the NEMA NU 2-2007 standards. The experimental results support that the developed dual-head DOI-TOF PET protoptype system has the MR-compatibility and the acceptable image quality from the incomplete TOF projection measurement.

关键词： image quality

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The application of compressed sensing method in photoacoustic image reconstruction

The application of compressed sensing method in photoacousti...

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International conference on Information Science and Technology (ICIST)

作者： Danfeng Hu Jiajun Wang Erxi Fang Wei Zhou Yue Zhou School of Electronics and Information Soochow University Suzhou Jiangsu China

Full-scanned photoacoustic data of the sample are needed to achieve better quality of the reconstructed photoacoustic image with filtered back projection algorithm. However, such a requirement is usually difficult to be satisfied due to the restrictions of the hardware conditions and spatial size. To tackle such a problem, a compressed sensing based method was proposed to recover the full-scanned photoacoustic data from the incomplete measurements. The results from the numerical simulation demonstrates that both the mean squared error and the peak signal-to-noise ratio(PSNR) of the image can significantly improved with our proposed method.

关键词： image reconstruction Compressed sensing Biomedical measurement Hardware Tomography Transforms

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The application of compressed sensing method in photoacoustic image reconstruction

The application of compressed sensing method in photoacousti...

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2014 4th IEEE International conference on Information Science and Technology

作者： Danfeng Hu Jiajun Wang Erxi Fang Wei Zhou Yue Zhou School of Electronics and Information Soochow University

关键词： domain image The application of compressed sensing method in photoacoustic image reconstruction

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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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IEEE International conference on image Processing

作者： Xue Li Yu-Jin Zhang Bin Shen Bao-Di Liu Electronic Engineering Tsinghua University Computer Science Purdue University Information and Control Engineering China University of Petroleum

ISBN: (纸本)9781479957521

A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error sparsity: 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.

关键词： Tag completion image annotation Low-rank Error sparsity LLE

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image quality assessment for extended-volume C-arm CT using a multi-turn reverse helix

Image quality assessment for extended-volume C-arm CT using ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Z. Yu A. Maier G. Lauritsch F. Vogt M. Schönborn C. Köhler J. Hornegger F. Noo Department of Radiology Friedrich-Alexander-University of Erlangen-Nuremberg Bavaria Germany

C-arm computed tomography (CT) with axially extended field-of-view is valuable when imaging a long organ is desired in the interventional room. However, current C-arm CT employs a circular short scan that only provides incomplete data and short axial coverage. To enable long-object 3D imaging capability on a C-arm system, a multi-turn reverse helix is an attractive solution for data acquisition. We have implemented this trajectory on a state-of-the-art multi-axis C-arm system and performed image reconstruction using our Fusion-RFDK method. This work evaluates these reconstruction results by comparing them with those obtained from a circular short scan. We observed comparable image quality between the two source trajectories.

关键词： Trajectory Phantoms Computed tomography image reconstruction image quality Torso

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Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization

Recovering Low-Rank and Sparse Matrices via Robust Bilateral...

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IEEE International conference on data Mining (ICDM)

作者： Fanhua Shang Yuanyuan Liu James Cheng Hong Cheng Dept. of Computer Science and Engineering The Chinese University of Hong Kong Dept. of Systems Engineering and Engineering Management

ISBN: (纸本)9781479943012

Recovering low-rank and sparse matrices from partial, incomplete or corrupted observations is an important problem in many areas of science and engineering. In this paper, we propose a scalable robust bilateral factorization (RBF) method to recover both structured matrices from missing and grossly corrupted data such as robust matrix completion (RMC), or incomplete and grossly corrupted measurements such as compressive principal component pursuit (CPCP). With the unified framework, we first present two robust trace norm regularized bilateral factorization models for RMC and CPCP problems, which can achieve an orthogonal dictionary and a robust data representation, simultaneously. Then, we apply the alternating direction method of multipliers to efficiently solve the RMC problems. Finally, we provide the convergence analysis of our algorithm, and extend it to address general CPCP problems. Experimental results verified both the efficiency and effectiveness of our RBF method compared with the state-of-the-art methods.

关键词： Sparse matrices Robustness Algorithm design and analysis Face Convergence image reconstruction Matrix decomposition

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The application of dictionary based compressed sensing for photoacoustic image

The application of dictionary based compressed sensing for p...

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Lili Zhou Jiajun Wang Danfeng Hu School of Electronic and Information Engineer Soochow University Suzhou China

ISBN: (纸本)9781479942145

Restrictions of the hardware conditions and spatial size usually limit the number of the measurements in photo acoustic imaging which will finally degrade the quality of the reconstructed image with the back projection algorithm. In order to recover larger number of measurements from incomplete ones, a compressed sensing (CS) based method was proposed. Different from most existed CS-based photoacoustic reconstruction method, the transform matrix for converting the measurement data to their compressed version is obtained by learning a dictionary with the K-SVD method. Visual assessment and quantitative evaluations in terms of the mean squared error (MSE) and the peak signal-to-noise ratio (PSNR) demonstrate the superiorities of our proposed method.

关键词： Abstracts Dictionaries Atmospheric measurements Particle measurements Discrete cosine transforms Time-domain analysis

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Extracting Watermark from 3D Prints

Extracting Watermark from 3D Prints

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International conference on Pattern Recognition

作者： Shuntaro Yamazaki Satoshi Kagami Masaaki Mochimaru Digital Human Research Center National Institute of Advanced Industrial Science and Technology Koto-ku Tokyo Japan

ISBN: (纸本)9781479952106

We propose a method of extracting watermark from 3D prints created from 3D mesh data. The watermark is embedded to the 3D mesh in the spectral domain using a robust, imperceptible, and informed algorithm based on the spread spectrum technique, and then extracted from 3D prints by reconstructing the 3D mesh homologous to the original. A suspect 3D mesh is reconstructed accurately and robustly from noisy and incomplete 3D scans of the 3D prints, by optimizing a sparse subset of the spectrum using a variant of the iterative closest point technique. The mesh is registered to the original mesh by construction, in terms of geometry and topology, which allows us to extract a suspect watermark by simple algebraic operations. Comprehensive experiments have been conducted to demonstrate the performance of our method using standard 3D datasets and multiple 3D printers. The proposed method significantly outperforms prior methods under different conditions in the practical scenario. The probability of false positive detection is kept less than 106 for the most cases of simulated and real data.

关键词： Three-dimensional displays Watermarking Shape Robustness image reconstruction Solid modeling Eigenvalues and eigenfunctions

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Structural Prior Enhanced Compressed Sensing for CT reconstruction with incomplete data

Structural Prior Enhanced Compressed Sensing for CT Reconstr...

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60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors

作者： Shen, Le Xing, Yuxiang Jin, Xin Nuctech Co Beijing Peoples R China Tsinghua Univ Dept Engn Phys Beijing Peoples R China Tsinghua Univ Minist Educ Key Lab Particle & Radiat Imaging Beijing 100084 Peoples R China

ISBN: (纸本)9781479905348

CT image reconstruction from incomplete projection data is a challenging problem. Among massive reconstruction methods, iterative reconstruction based on compressed sensing (CS) is a promising one that enables us to accurately recovery signals from highly under-sample data when the signals have a sparse representation, which usually can be done by the constrained l(1) minimization. The total variation (TV) minimization is a commonly used sparsity constraint, which assumes the target image is piece-wise constant. TV based CS algorithm has been successfully applied to solve many computed tomography problems, such as few views and interior reconstruction. In this work, we proposed a novel CS algorithm combined with a prior image to enhance the TV sparsity, namely structural prior enhanced compressed sensing (SPECS). Numerical simulation indicates SPECS is effective and robust for many kinds of incomplete data cases.

关键词： Compressed sensing

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Efficient image reconstruction in fluorescence diffuse opitcal tomography (fDOT) using data and solution compression

Efficient image reconstruction in fluorescence diffuse opitc...

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conference on Diffuse Optical Imaging iv

作者： Correia, Teresa Rudge, Timothy Arridge, Simon UCL Dept Comp Sci London WC1E 6BT England

ISBN: (纸本)9780819496485

Current fluorescence diffuse optical tomography (fDOT) systems can provide large data sets and, in addition, the unknown parameters to be estimated are so numerous that the sensitivity matrix is too large to store. Alternatively, iterative methods can be used, but they can be extremely slow at converging when dealing with large matrices. A few approaches suitable for the reconstruction of images from very large data sets have been developed. However, they either require explicit construction of the sensitivity matrix, suffer from slow computation times or can only be applied to restricted geometries. We introduce a method for fast reconstruction in fDOT with large data and solution spaces, which preserves the resolution of the forward operator whilst compressing its representation. The method does not require construction of the full matrix, and thus, allows storage and direct inversion of the explicitly constructed compressed system matrix. The method is tested using simulated data. Results show that the fDOT image reconstruction problem can be effectively compressed, without siginificant loss of information and with the added advantage of reducing image noise.

关键词： Fluorescence inverse problems data compression wavelets

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