Purpose: Varian TrueBeam version 2.0 comes with a new inline 2.5MV beam modality for image guided patient setup. In this work we develop an iterative volumetric image reconstruction technique specific to the beam and ...
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Purpose: Varian TrueBeam version 2.0 comes with a new inline 2.5MV beam modality for image guided patient setup. In this work we develop an iterative volumetric image reconstruction technique specific to the beam and investigate the possibility of obtaining metal artifact free CBCT images using the new imaging modality. Methods: An iterative reconstruction algorithm with a sparse representation constraint based on dictionary learning is developed, in which both sparse projection and low dose rate (10 MU/min) are considered. Two CBCT experiments were conducted using the newly available 2.5MV beam on a Varian TrueBeam linac. First, a Rando anthropomorphic head phantom with and without a copper bar inserted in the center was scanned using both 2.5MV and kV (100kVp) beams. In a second experiment, an MRI phantom with many coils was scanned using 2.5MV, 6MV, and kV (100kVp) beams. Imaging dose and the resultant image quality is studied. Results: Qualitative assessment suggests that there were no visually detectable metal artifacts in MV CBCT images, compared with significant metal artifacts in kV CBCT images, especially in the MRI phantom. For a region near the metal object in the head phantom, the 2.5MV CBCT gave a more accurate quantification of the electron density compared with kV CBCT, with a ∼50% reduction in mean HU error. As expected, the contrast between bone and soft-tissue in 2.5MV CBCT decreased compared with kV CBCT. Conclusion: On-board CBCT imaging with the new 2.5MV beam can effectively reduce metal artifacts, although with a reduced softtissue contrast. Combination of kV and MV scanning may lead to metal artifact free CBCT images with uncompromised soft-tissue contrast.
Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficien...
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Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.
Age estimation has received extensively interest in computer vision field due to its wide applications, i.e. human behavior analysis, video content recognition, etc. Hence, many classification and regression approache...
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The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter f...
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The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter for multi-sensor target tracking problem, a novel average weight optimization Rao-Blackwellised particle filtering al- gorithm is proposed. Combining with the kinetic equation of target state evolution, RBPF is used as the basic es- timator of algorithm realization. For the rational utiliza- tion from multi-sensor observations and the reduction of the adverse influence from random observations noise in measuring process of particles weight, the average weight optimization strategy is used to improve the reliability and stability of particle weight variance. In addition, we give the concrete flow of RBPF in average weight optimization strategy. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
Digital subtraction angiography has become one of the most important approaches to artery disease diagnosis and treatmentDoctors implement diagnose and treatment by subjective analysis of the DSA series,and the result...
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Digital subtraction angiography has become one of the most important approaches to artery disease diagnosis and treatmentDoctors implement diagnose and treatment by subjective analysis of the DSA series,and the results are always dependent on doctors' experienceThe application of color-coded imaging technology makes it convenient to identify images and provides additional physiology information auxiliary diagnosis and treatmentBefore implementing color-coded imaging technology on DSA series,we preprocess the images with several mutiscale spatial filters to remove noises and enhance vessel structures to make the results readable and clear.
This paper proposes a three-layer model for full frame video *** is practical for real time full frame processing where the smoothness of intentional camera motion is *** undefined pixels in stabilized frame are fille...
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This paper proposes a three-layer model for full frame video *** is practical for real time full frame processing where the smoothness of intentional camera motion is *** undefined pixels in stabilized frame are filled by pixels in previous frames *** traditional methods that all neighboring frames are required to be stored and registered with current frame,the proposed algorithm only stores single updated mosaic image for video *** runs significantly faster than previous *** efficacy has been demonstrated by real experiments.
In the studying of fibers microstructure of brain white matter,many reconstruction methods have been proposed to interpret the diffusion-weighted signalThose methods can be categorized into modelbased and model-free m...
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In the studying of fibers microstructure of brain white matter,many reconstruction methods have been proposed to interpret the diffusion-weighted signalThose methods can be categorized into modelbased and model-free methodsIn this paper,the diffusion configuration of water molecules are discussed,and two questions are put forward to analyze the performance of the current algorithms about diffusion configuration.
Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to provide solutions to many problems in computer vision. In this paper, we consider GM as a clustering problem in...
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ISBN:
(纸本)9781479939046
Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to provide solutions to many problems in computer vision. In this paper, we consider GM as a clustering problem in an association graph whose nodes represent candidate correspondences between two graphs to be matched. And we take the dense subgraph as a good prior for correct correspondences, thus we propose a label propagation approach to expand the dense subgraph to resolve the whole cluster. The label propagation approach is achieved by an affinity-preserving manifold ranking algorithm with a dynamic label vector which enforces the matching constraints. And the matching constraints is introduced through a doubly-stochastic normalization procedure. Extensive experiments demonstrate that our algorithm outperforms the state-of-the-art GM algorithms especially in the presence of outliers and deformation.
An objective approach is proposed to measure the image degradation caused by optical transmission effects of atmospheric turbulence. Comparisons of the proposed measure with existing objective image quality measures a...
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ISBN:
(纸本)9781479954599
An objective approach is proposed to measure the image degradation caused by optical transmission effects of atmospheric turbulence. Comparisons of the proposed measure with existing objective image quality measures are performed and the correlations between the measure and subjective ratings are quantified. Experiments on wind-tunnel images and simulated turbulence-degraded images produce good results. The proposed measure may serve as a complement to state-of-the-art measures in evaluating image degradation caused by turbulence. It can also be applied as a criterion for frame selection and iteration termination for iterative restoration algorithms or help evaluate the performances of image restoration algorithms.
In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the...
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In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the goal of tracking is to search the unlabeled sample that is the most relevant with existing labeled nodes by manifold ranking algorithm. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show the proposed algorithm outperforms six state-of-the-art methods in terms of accuracy and robustness.
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