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  • Articles  (239)
  • 2015-2019  (239)
  • 2015  (239)
  • Technology  (239)
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  • Articles  (239)
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  • 2015-2019  (239)
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  • 1
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    A Constrained Variable Projection Reconstruction Method for Photoacoustic Computed Tomography Without Accurate Knowledge of Transducer Responses (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. When the imaging system employs conventional piezoelectric ultrasonic transducers, the ideal photoacoustic (PA) signals are degraded by the transducers' acousto-electric impulse responses (EIRs) during the measurement process. If unaccounted for, this can degrade the accuracy of the reconstructed image. In principle, the effect of the EIRs on the measured PA signals can be ameliorated via deconvolution; images can be reconstructed subsequently by application of a reconstruction method that assumes an idealized EIR. Alternatively, the effect of the EIR can be incorporated into an imaging model and implicitly compensated for during reconstruction. In either case, the efficacy of the correction can be limited by errors in the assumed EIRs. In this work, a joint optimization approach to PACT image reconstruction is proposed for mitigating errors in reconstructed images that are caused by use of an inaccurate EIR. The method exploits the bi-linear nature of the imaging model and seeks to refine the measured EIR during the process of reconstructing the sought-after absorbed optical energy density. Computer-simulation and experimental studies are conducted to investigate the numerical properties of the method and demonstrate its value for mitigating image distortions and enhancing the visibility of fine structures.
    Print ISSN: 0278-0062
    Electronic ISSN: 1558-254X
    Topics: Medicine , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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  • 2
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    Contour-Driven Atlas-Based Segmentation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images.
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    Electronic ISSN: 1558-254X
    Topics: Medicine , Technology
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  • 3
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    LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.
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    Topics: Medicine , Technology
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  • 4
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    Cramér-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: The Cramér-Rao lower bound for the estimation of the affine transformation parameters in a multivariate heteroscedastic errors-in-variables model is derived. The model is suitable for feature-based image registration in which both sets of control points are localized with errors whose covariance matrices vary from point to point. With focus given to the registration of fluorescence microscopy images, the Cramér-Rao lower bound for the estimation of a feature’s position (e.g., of a single molecule) in a registered image is also derived. In the particular case where all covariance matrices for the localization errors are scalar multiples of a common positive definite matrix (e.g., the identity matrix), as can be assumed in fluorescence microscopy, then simplified expressions for the Cramér-Rao lower bound are given. Under certain simplifying assumptions these expressions are shown to match asymptotic distributions for a previously presented set of estimators. Theoretical results are verified with simulations and experimental data.
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    Topics: Medicine , Technology
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  • 5
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    Erratum to “Noise Suppression for Dual-Energy CT Through Entropy Minimization” (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: Presents corrections to, ???Noise suppression for dual-energy CT through entropy minimization,??? (Petrongolo, M. and Zhu, L., IEEE Trans. Med. Imag., vol. 34, no. 11, pp. 2286???2297, Nov. 2015).
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    Topics: Medicine , Technology
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  • 6
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    Automated, Depth-Resolved Estimation of the Attenuation Coefficient From Optical Coherence Tomography Data (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: We present a method for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) data. In contrast to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and the attenuation coefficient while considering the confocal function and sensitivity fall-off, which are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We also show that further improvement of the estimated attenuation coefficient is possible by formulating image denoising as a convex optimization problem that we term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated data, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a novel way to improve OCT contrast for better tissue characterization through quantitative imaging.
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    Topics: Medicine , Technology
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  • 7
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    Low-Rank Atlas Image Analyses in the Presence of Pathologies (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-04
    Description: We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a “healthy” version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. For unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration's atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012.
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    Topics: Medicine , Technology
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  • 8
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    Retinal Artery-Vein Classification via Topology Estimation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-12-01
    Description: We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.
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    Topics: Medicine , Technology
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  • 9
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    IEEE Transactions on Medical Imaging publication information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2015-11-27
    Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.
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    Topics: Medicine , Technology
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  • 10
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    Pixel Bleeding Correction in Laser Scanning Luminescence Imaging Demonstrated Using Optically Stimulated Luminescence (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-11-27
    Description: This paper describes and investigates the performance of an algorithm to correct for “pixel bleeding” caused by slow luminescence centers in laser scanning imaging (e.g., X-ray imaging using photostimulable phosphors and 2D dosimetry using optically stimulated luminescence). The algorithm is based on a deconvolution procedure that takes into account the lifetime of the slow luminescence center and is further constrained by the detection of fast and slow luminescence centers and combining rows scanned in opposite directions. The algorithm was tested using simulated data and demonstrated experimentally by applying it to image reconstruction of two types of ${rm Al}_{2} {rm O} _{3}$ X-ray detector films ( ${rm Al}_{2} {rm O} _{3}$ :C and ${rm Al}_{2} {rm O} _{3}$ :C,Mg), whose use in 2D dosimetry in conjunction with laser-scanning readout has so far been prevented by slow luminescence centers (F-centers, 35 ms lifetime). We show that the algorithm allows the readout of ${rm Al}_{2} {rm O} _{3}$ film detectors 300–500 times faster than generally allowed considering the lifetime of the main luminescence centers. By relaxing the stringent requirements on the detector's luminescence lifetime, the algorithm opens the possibility of using new materials in 2D dosimetry as well as other laser scanning applications, such as X-ray imaging using storage phosphors and scanning confocal microscopy, although the effect of the noise introduced must be investigated for each specific application.
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    Topics: Medicine , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE)
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