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  • Articles  (957)
  • 2015-2019  (957)
  • IEEE Transactions on Medical Imaging  (957)
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  • Technology  (957)
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  • Articles  (957)
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  • 1
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    Table of contents (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Print ISSN: 0278-0062
    Electronic ISSN: 1558-254X
    Topics: Medicine , Technology
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  • 2
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    IEEE Transactions on Medical Imaging publication information (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
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    Topics: Medicine , Technology
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  • 3
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    Neuron Segmentation With High-Level Biological Priors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: We present a novel approach to the problem of neuron segmentation in image volumes acquired by an electron microscopy. Existing methods, such as agglomerative or correlation clustering, rely solely on boundary evidence and have problems where such an evidence is lacking (e.g., incomplete staining) or ambiguous (e.g., co-located cell and mitochondria membranes). We investigate if these difficulties can be overcome by means of sparse region appearance cues that differentiate between pre- and postsynaptic neuron segments in mammalian neural tissue. We combine these cues with the traditional boundary evidence in the asymmetric multiway cut (AMWC) model, which simultaneously solves the partitioning and the semantic region labeling problems. We show that AMWC problems over superpixel graphs can be solved to global optimality with a cutting plane approach, and that the introduction of semantic class priors leads to significantly better segmentations.
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  • 4
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    2.5-D Extended Field-of-View Ultrasound (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Recently, the growing emphasis on medical ultrasound (US) has led to a rapid development of US extended field-of-view (EFOV) techniques. US EFOV techniques can be classified into three categories: 2-D US EFOV, 3-D US, and 3-D US EFOV. In this paper, we propose a novel EFOV method called 2.5-D US EFOV that combines both the advantages of the 2-D US EFOV and the 3-D US by generating a panorama on a curved image plane guided by a curved scanning trajectory of the US probe. In 2.5-D US EFOV, the real-time position and orientation of the US image plane can be recorded via an electromagnetic spatial sensor attached to the probe. The scanning direction is not necessarily straight and can be curved according to the regions of interest (ROI). To form the curved panorama, an image cutting method is proposed. Finally, the curved panorama is rendered in a 3-D space using a surface rendering based on a texture mapping technique. This allows 3-D measurements of lines and angles. Phantom experiments demonstrated that 2.5-D US EFOV images could show anatomical structures of ROI accurately and rapidly. The overall average errors for the distance and angle measurements are −0.097 ± 0.128 cm (−1% ± 1.2%) and 1.50° ± 1.60° (1.9% ± 2%), respectively. A typical extended US image can be reconstructed from 321 B-scans images within 3 s. The satisfying quantitative result on the spinal tissues of a scoliosis subject demonstrates that our system has potential applications in the assessment of musculoskeletal issues.
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  • 5
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    Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in ${D}_{98%}$ , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.
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  • 6
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    Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model, which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, and 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms $K$ -means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to the resting-state fMRI data, our method successfully identifies modular organization in the resting-statenetworksin consistencywith other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.
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  • 7
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    Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18 F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.
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  • 8
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    Design and Performance of a 1 mm 3 Resolution Clinical PET System Comprising 3-D Position Sensitive Scintillation Detectors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: We are developing a 1-mm 3 resolution, high-sensitivity positron emission tomography (PET) system for loco-regional cancer imaging. The completed system will comprise two $16.5times 10$ cm detector panels and contain 4 608 position sensitive avalanche photodiodes (PSAPDs) coupled to arrays of $0.9times 0.9times 1$ mm 3 LYSO crystal elements for a total of 294 912 crystal elements. For the first time, this paper summarizes the design and reports the performance of a significant portion of the final clinical PET system, comprising 1 536 PSAPDs, 98 304 crystal elements, and an active field-of-view (FOV) of $16.5times3.3$ cm. The sub-system performance parameters, such as energy, time, and spatial resolutions are predictive of the performance of the final system due to the modular design. Analysis of the multiplexed crystal flood histograms shows 84% of the crystal elements have〉99% crystal identification accuracy. The 511 keV photopeak energy resolution was 11.34±0.06% full-width half maximum (FWHM), and coincidence timing resolution was 13.92 ± 0.01 ns FWHM at 511 keV. The spatial resolution was measured using maximum likelihood expectation maximization reconstruction of a grid of point sources suspended in warm background. The averaged resolution over the central 6 cm of the FOV is 1.01 ± 0.13 mm in the X-direction, 1.84 ± 0.20 mm in the Y-direction, and 0.84 ± 0.11 mm in the Z-direction. Quantitative analysis of acquired micro-Derenzo phantom images shows better than 1.2 mm resolution at the center of the FOV, with subsequent resolution degradation in the y-direction toward the edge of the FOV caused by limited angle tomography effec- s.
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  • 9
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    High-Permittivity Pad Design for Dielectric Shimming in Magnetic Resonance Imaging Using Projection-Based Model Reduction and a Nonlinear Optimization Scheme (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Inhomogeneities in the transmit radio frequency magnetic field ( ${text{B}}_{1}^{+}$ ) reduce the quality of magnetic resonance (MR) images. This quality can be improved by using high-permittivity pads that tailor the ${text{B}}_{1}^{+}$ fields. The design of an optimal pad is application-specific and not straightforward and would therefore benefit from a systematic optimization approach. In this paper, we propose such a method to efficiently design dielectric pads. To this end, a projection-based model order reduction technique is used that significantly decreases the dimension of the design problem. Subsequently, the resulting reduced-order model is incorporated in an optimization method in which a desired field in a region of interest can be set. The method is validated by designing a pad for imaging the cerebellum at 7 T. The optimal pad that is found is used in an MR measurement to demonstrate its effectiveness in improving the image quality.
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  • 10
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    40th International Conference of the IEEE Engineering in Medicine and Biology Society (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
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    Topics: Medicine , Technology
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