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  • Articles  (4,651)
  • Institute of Electrical and Electronics Engineers (IEEE)  (4,651)
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  • 1
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    A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences From the Same Virtual Patients (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-06
    Description: The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database.
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  • 2
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    Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-06
    Description: Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences. Our new pipeline notably involves a much easier registration problem to reduce potential artifacts, and takes advantage of mesh correspondences to generate new data from a given case without additional registration. The output sequences are thoroughly examined in terms of quality and usability on a given application: the assessment of myocardial viability, via the generation of 465 synthetic cine MR sequences (15 healthy and 450 with pathological tissue viability [random location, extent, and grade, up to myocardial infarct]). We demonstrate that: 1) our methodology improves the state-of-the-art algorithms in terms of realism and accuracy of the simulated images and 2) our methodology is well-suited for the generation of large databases at small computational cost.
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  • 3
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    Adversarial Stain Transfer for Histopathology Image Analysis (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-06
    Description: It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasing access to digitized histopathology images, color variation and its implications have become a critical issue. These variations are the result of not only a variety of factors involved in the preparation of tissue slides but also in the digitization process itself. Consequently, different strategies have been proposed to alleviate stain-related tissue inconsistencies in automatic image analysis systems. Such techniques generally rely on collecting color statistics to perform color matching across images. In this work, we propose a different approach for stain normalization that we refer to as stain transfer. We design a discriminative image analysis model equipped with a stain normalization component that transfers stains across datasets. Our model comprises a generative network that learns data set-specific staining properties and image-specific color transformations as well as a task-specific network (e.g., classifier or segmentation network). The model is trained end-to-end using a multi-objective cost function. We evaluate the proposed approach in the context of automatic histopathology image analysis on three data sets and two different analysis tasks: tissue segmentation and classification. The proposed method achieves superior results in terms of accuracy and quality of normalized images compared to various baselines.
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  • 4
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    End-to-End Adversarial Retinal Image Synthesis (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-06
    Description: In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.
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  • 5
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    Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-06
    Description: Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.
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  • 6
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    A New 4-D Nonlocal Transform-Domain Filter for 3-D Magnetic Resonance Images Denoising (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: The simultaneous removal of noise and preservation of the integrity of 3-D magnetic resonance (MR) images is a difficult and important task. In this paper, we consider characterizing MR images with 3-D operators, and present a novel 4-D transform-domain method termed ‘modified nonlocal tensor-SVD (MNL-tSVD)’ for MR image denoising. The proposed method is based on the grouping, hard-thresholding and aggregation paradigms, and can be viewed as a generalized nonlocal extension of tensor-SVD (t-SVD). By keeping MR images in its natural three-dimensional form, and collaboratively filtering similar patches, MNL-tSVD utilizes both the self-similarity property and 3-D structure of MR images to preserve more actual details and minimize the introduction of new artifacts. We show the adaptability of MNL-tSVD by incorporating it into a two-stage denoising strategy with a few adjustments. In addition, analysis of the relationship between MNL-tSVD and current the state-of-the-art 4-D transforms is given. Experimental comparisons over simulated and real brain data sets at different Rician noise levels show that MNL-tSVD can produce competitive performance compared with related approaches.
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  • 7
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    Table of contents (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
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  • 8
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    Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper, we present a novel method to detect conditional associations between imaging genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, and then use multiple testing to detect significant group level associations (e.g., regions of interest-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with G-PDC than distance correlation, Pearson’s correlation, and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The MATLAB code is available at https://sites.google.com/site/jianfang86/gPDC .
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  • 9
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    Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: We present a multi-scale approach of tumor modeling in order to predict its evolution during radiotherapy. Within this context we focus on three different scales of tumor modeling: microscopic (individual cells in a voxel), mesoscopic (population of cells in a voxel) and macroscopic (whole tumor), with transition interfaces between these three scales. At the cellular level, the description is based on phase transfer probabilities in the cellular cycle. At the mesoscopic scale we represent populations of cells according to different stages in a cell cycle. Finally, at the macroscopic scale, the tumor description is based on the use of FDG PET image voxels. These three scales exist naturally: biological data are collected at the macroscopic scale, but the pathological behavior of the tumor is based on an abnormal cell-cycle at the microscopic scale. On the other hand, the introduction of a mesoscopic scale is essential in order to reduce the gap between the two extreme, in terms of resolution, description levels. It also reduces the computational burden of simulating a large number of individual cells. As an application of the proposed multi-scale model, we simulate the effect of oxygen on tumor evolution during radiotherapy. Two consecutive FDG PET images of 17 rectal cancer patients undergoing radiotherapy are used to simulate the tumor evolution during treatment. The simulated results are compared with those obtained on a third FDG PET image acquired two weeks after the beginning of the treatment.
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  • 10
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    Estimating Maximum Target Registration Error Under Uniform Restriction of Fiducial Localization Error in Image Guided System (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: In this paper, we investigate the estimation of the maximum target registration error (TRE) magnitude of the target location while using point-based rigid registration in the image guided system. Under the uniform restriction of fiducial localization error (FLE) magnitude, we explicitly formulate the estimation as an optimization problem. Through analyzing the approximated problem which assumes the rigidity of the fiducial set holds with the perturbation of FLE, we present a strict lower bound for the maximum TRE magnitude. The simulations show that the lower bound is close to the actual maximum TRE magnitude for the target locations lying far away from the fiducial points. Unlike the expected TRE magnitude in which all fiducial points contribute, the lower bound is only related to the fiducial points serving as the vertices of the convex hull of the fiducial set. Our analysis provides a new perspective of investigating the problem of TRE estimation and is helpful for the surgeons to learn about the worst situation during using the image guided system.
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  • 11
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    Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Brain tumors are the most common malignant neurologic tumors with the highest mortality and disability rate. Because of the delicate structure of the brain, the clinical use of several commonly used biopsy diagnosis is limited for brain tumors. Radiomics is an emerging technique for noninvasive diagnosis based on quantitative medical image analyses. However, current radiomics techniques are not standardized regarding feature extraction, feature selection, and decision making. In this paper, we propose a sparse representation-based radiomics (SRR) system for the diagnosis of brain tumors. First, we developed a dictionary learning- and sparse representation-based feature extraction method that exploits the statistical characteristics of the lesion area, leading to fine and more effective feature extraction compared with the traditional explicitly calculation-based methods. Then, we set up an iterative sparse representation method to solve the redundancy problem of the extracted features. Finally, we proposed a novel multi-feature collaborative sparse representation classification framework that introduces a new coefficient of regularization term to combine features from multi-modal images at the sparse representation coefficient level. Two clinical problems were used to validate the performance and usefulness of the proposed SRR system. One was the differential diagnosis between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and the other was isocitrate dehydrogenase 1 estimation for gliomas. The SRR system had superior PCNSL and GBM differentiation performance compared with some advanced imaging techniques and yielded 11% better performance for estimating IDH1 compared with the traditional radiomics methods.
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  • 12
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    Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: We present the first in vivo images of anisotropic conductivity distribution in the human head, measured at a frequency of approximately 10 Hz. We used magnetic resonance electrical impedance tomography techniques to encode phase changes caused by current flow within the head via two independent electrode pairs. These results were then combined with diffusion tensor imaging data to reconstruct full anisotropic conductivity distributions in 5-mm-thick slices of the brains of two participants. Conductivity values recovered in this paper were broadly consistent with literature values. We anticipate that this technique will be of use in many areas of neuroscience, most importantly in functional imaging via inverse electroencephalogram. Future studies will involve pulse sequence acceleration to maximize brain coverage and resolution.
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  • 13
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    A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation (2018)
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    Publication Date: 2018-04-04
    Description: The most commonly used evaluation metrics for quality assessment of retinal vessel segmentation are sensitivity, specificity, and accuracy, which are based on pixel-to-pixel matching. However, due to the inter-observer problem that vessels annotated by different observers vary in both thickness and location, pixel-to-pixel matching is too restrictive to fairly evaluate the results of vessel segmentation. In this paper, the proposed skeletal similarity metric is constructed by comparing the skeleton maps generated from the reference and the source vessel segmentation maps. To address the inter-observer problem, instead of using a pixel-to-pixel matching strategy, each skeleton segment in the reference skeleton map is adaptively assigned with a searching range whose radius is determined based on its vessel thickness. Pixels in the source skeleton map located within the searching range are then selected for similarity calculation. The skeletal similarity consists of a curve similarity, which measures the structural similarity between the reference and the source skeleton maps and a thickness similarity, which measures the thickness consistency between the reference and the source vessel segmentation maps. In contrast to other metrics that provide a global score for the overall performance, we modify the definitions of true positive, false negative, true negative, and false positive based on the skeletal similarity, based on which sensitivity, specificity, accuracy, and other objective measurements can be constructed. More importantly, the skeletal similarity metric has better potential to be used as a pixelwise loss function for training deep learning models for retinal vessel segmentation. Through comparison of a set of examples, we demonstrate that the redefined metrics based on the skeletal similarity are more effective for quality evaluation, especially with greater tolerance to the inter-observer problem.
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  • 14
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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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  • 15
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    40th International Conference of the IEEE Engineering in Medicine and Biology Society (2018)
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    Publication Date: 2018-04-04
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  • 16
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    IEEE EMBS Micro and Nanotechnology Conference in Medicine (2018)
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    Publication Date: 2018-04-04
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  • 17
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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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  • 18
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    Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning (2018)
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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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  • 19
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    Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors (2018)
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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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  • 20
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    Online Robust Projective Dictionary Learning: Shape Modeling for MR-TRUS Registration (2018)
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    Publication Date: 2018-04-04
    Description: Robust and effective shape prior modeling from a set of training data remains a challenging task, since the shape variation is complicated, and shape models should preserve local details as well as handle shape noises. To address these challenges, a novel robust projective dictionary learning (RPDL) scheme is proposed in this paper. Specifically, the RPDL method integrates the dimension reduction and dictionary learning into a unified framework for shape prior modeling, which can not only learn a robust and representative dictionary with the energy preservation of the training data, but also reduce the dimensionality and computational cost via the subspace learning. In addition, the proposed RPDL algorithm is regularized by using the $ell _{1}$ norm to handle the outliers and noises, and is embedded in an online framework so that of memory and time efficiency. The proposed method is employed to model prostate shape prior for the application of magnetic resonance transrectal ultrasound registration. The experimental results demonstrate that our method provides more accurate and robust shape modeling than the state-of-the-art methods do. The proposed RPDL method is applicable for modeling other organs, and hence, a general solution for the problem of shape prior modeling.
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  • 21
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    IEEE Transactions on Medical Imaging information for authors (2018)
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    Publication Date: 2018-04-04
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  • 22
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    Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model (2018)
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    Publication Date: 2018-04-04
    Description: Hyperpolarized MRI with 13 C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of 13 C data, both for clinical and research purposes, typically relies on the use of region-of-interest analysis to detect and compare regions of altered metabolism. However, it is not clear how this should be determined from the five-dimensional data produced and most standard methodologies are unable to exploit the multidimensional nature of the data. Here we propose a solution to the novel problem of 13 C image segmentation using a hybrid Markov random field model with continuous fuzzy logic. The algorithm fully utilizes the multi-dimensional data format in order to classify each voxel into one of six distinct classes based on its metabolic characteristics. Bayesian priors fully incorporate spatial, temporal and ratiometric contextual information whilst image contrast from multiple spectral dimensions are considered concurrently by using an analogy from color image segmentation. Performance of the algorithm is demonstrated on in silico data, where the superiority of the approach over a reference thresholding method is consistently observed. Application to in vivo animal data from a pre-clinical subcutaneous tumor model illustrates the ability of the MRF algorithm to successfully detect tumor location whilst avoiding image artifacts. This work has the potential to assist the analysis of human hyperpolarized 13 C data in the future.
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    2.5-D Extended Field-of-View Ultrasound (2018)
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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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    Ultrafast Ultrasound Imaging With Cascaded Dual-Polarity Waves (2018)
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    Publication Date: 2018-04-04
    Description: Ultrafast ultrasound imaging using plane or diverging waves, instead of focused beams, has advanced greatly the development of novel ultrasound imaging methods for evaluating tissue functions beyond anatomical information. However, the sonographic signal-to-noise ratio (SNR) of ultrafast imaging remains limited due to the lack of transmission focusing, and thus insufficient acoustic energy delivery. We hereby propose a new ultrafast ultrasound imaging methodology with cascaded dual-polarity waves (CDWs), which consists of a pulse train with positive and negative polarities. A new coding scheme and a corresponding linear decoding process were thereby designed to obtain the recovered signals with increased amplitude, thus increasing the SNR without sacrificing the frame rate. The newly designed CDW ultrafast ultrasound imaging technique achieved higher quality B-mode images than coherent plane-wave compounding (CPWC) and multiplane wave (MW) imaging in a calibration phantom, ex vivo pork belly, and in vivo human back muscle. CDW imaging shows a significant improvement in the SNR (10.71 dB versus CPWC and 7.62 dB versus MW), penetration depth (36.94% versus CPWC and 35.14% versus MW), and contrast ratio in deep regions (5.97 dB versus CPWC and 5.05 dB versus MW) without compromising other image quality metrics, such as spatial resolution and frame rate. The enhanced image qualities and ultrafast frame rates offered by CDW imaging beget great potential for various novel imaging applications.
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    Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper, the authors aim to develop a computerized segmentation system, which uses a seed position as the only priori of the problem. A two-stage segmentation approach has been proposed incorporating shape information of training masses. At the first stage, a new adaptive region growing algorithm is used to give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is determined using a Gaussian mixture model based on the volume and circularity of the training masses. In the second stage, a novel geometric edge-based deformable model is introduced using the result of the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value < 0.02). Moreover, the resulting mean Dice was significantly (p-value < 0.001) better than that of the distance regularized level set evolution method (0.52 ± 0.27). The supervised method presented in this paper achieved accurate mass segmentation results in terms of Dice measure. The suggested segmentation method can be utilized in two aspects: 1) to automatically measure the change in volume of breast lesions over time and 2) to extract features for a computer aided detection or diagnosis system.
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    Automated Cell Segmentation for Quantitative Phase Microscopy (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-04-04
    Description: Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.
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    Neuron Segmentation With High-Level Biological Priors (2018)
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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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    Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity (2018)
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    Publication Date: 2018-04-04
    Description: Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.
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    Penalized-Likelihood Reconstruction With High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging (2018)
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    Publication Date: 2018-04-04
    Description: We present a novel reconstruction algorithm based on a general cone-beam CT forward model, which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function, which incorporates models of blur and correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared with deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPL-BC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared with deblurring followed by FDK, a model-based method without blur, and a model-based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test-bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model-based methods without blur and/or correlation to a registered $mu $ CT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including trabecular thickness (Tb.Th.) were computed for each reconstruction approach as well as the $mu $ CT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as compared with the $mu $ CT derived value of 0.193 mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271 mm, 0.309 mm, and 0.335 mm, respect- vely).
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    Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble (2018)
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    Publication Date: 2018-04-04
    Description: Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.
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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)
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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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    IEEE Transactions on Medical Imaging publication information (2018)
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    Publication Date: 2018-04-04
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    Evaluation of Parallel Level Sets and Bowsher’s Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction (2018)
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    Publication Date: 2018-02-07
    Description: In this article, we evaluate Parallel Level Sets (PLS) and Bowsher’s method as segmentation-free anatomical priors for regularized brain positron emission tomography (PET) reconstruction. We derive the proximity operators for two PLS priors and use the EM-TV algorithm in combination with the first order primal-dual algorithm by Chambolle and Pock to solve the non-smooth optimization problem for PET reconstruction with PLS regularization. In addition, we compare the performance of two PLS versions against the symmetric and asymmetric Bowsher priors with quadratic and relative difference penalty function. For this aim, we first evaluate reconstructions of 30 noise realizations of simulated PET data derived from a real hybrid positron emission tomography/magnetic resonance imaging (PET/MR) acquisition in terms of regional bias and noise. Second, we evaluate reconstructions of a real brain PET/MR data set acquired on a GE Signa time-of-flight PET/MR in a similar way. The reconstructions of simulated and real 3D PET/MR data show that all priors were superior to post-smoothed maximum likelihood expectation maximization with ordered subsets (OSEM) in terms of bias-noise characteristics in different regions of interest where the PET uptake follows anatomical boundaries. Our implementation of the asymmetric Bowsher prior showed slightly superior performance compared with the two versions of PLS and the symmetric Bowsher prior. At very high regularization weights, all investigated anatomical priors suffer from the transfer of non-shared gradients.
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    Table of contents (2018)
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    Publication Date: 2018-02-09
    Description: Presents the table of contents for this issue of this publication.
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    Extracorporeal Stimulation of Sacral Nerve Roots for Observation of Pelvic Autonomic Nerve Integrity: Description of a Novel Methodological Setup (2018)
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    Publication Date: 2018-02-21
    Description: Introduction: Neurophysiologic monitoring can improve autonomic nerve sparing during critical phases of rectal cancer surgery. Objectives: To develop a system for extracorporeal stimulation of sacral nerve roots. Methods: Dedicated software controlled a ten-electrode stimulation array by switching between different electrode configurations and current levels. A built-in impedance and current level measurement assessed the effectiveness of current injection. Intra-anal surface electromyography (sEMG) informed on targeting the sacral nerve roots. All tests were performed on five pig specimens. Results: During switching between electrode configurations, the system delivered 100% of the set current (25 mA, 30 Hz, 200 μs cathodic pulses) in 93% of 250 stimulation trains across all specimens. The impedance measured between single stimulation array contacts and corresponding anodes across all electrode configurations and specimens equaled 3.7 ± 2.5 kΩ. The intra-anal sEMG recorded a signal amplitude increase as previously observed in the literature. When the stimulation amplitude was tested in the range from 1 to 21 mA using the interconnected contacts of the stimulation array and the intra-anal anode, the impedance remained below 250 Ω and the system delivered 100% of the set current in all cases. Intra-anal sEMG showed an amplitude increase for current levels exceeding 6 mA. Conclusion: The system delivered stable electric current, which was proved by built-in impedance and current level measurements. Intra-anal sEMG confirmed the ability to target the branches of the autonomous nervous system originating from the sacral nerve roots. Significance: Stimulation outside of t- e operative field during rectal cancer surgery is feasible and may improve the practicality of pelvic intraoperative neuromonitoring.
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    Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses with biomechanical models not operating in real-time for man–machine interfacing. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e., predicting accurate joint moments during six unseen tasks and one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-t- me bio-feedback of internal mechanical forces and direct patient-machine interfacing.
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    Critical Closing Pressure During Controlled Increase in Intracranial Pressure—Comparison of Three Methods (2018)
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    Publication Date: 2018-02-21
    Description: Goal : Critical closing pressure (CrCP) is the arterial blood pressure (ABP) threshold, below which small arterial vessels collapse and cerebral blood flow ceases. Here, we aim to compare three methods for CrCP estimation in scenario of a controlled increase in intracranial pressure (ICP), induced by infusion tests performed in patients with suspected normal pressure hydrocephalus (NPH). Methods : Computer recordings of directly-measured ICP, ABP, and transcranial Doppler cerebral blood flow velocity (CBFV), from 37 NPH patients undergoing infusion tests, were retrospectively analyzed. The CrCP was calculated with three methods: one with the first harmonics ratio of the pulse waveforms of ABP and CBFV (CrCPA) and two methods based on a model of cerebrovascular impedance, as functions of both cerebral perfusion pressure (CrCPinv), and of ABP (CrCPninv). Conclusion : All methods give similar results in response to ICP changes. In the case of individual CrCP measurements for each patient, CrCPA may provide negative, nonphysiological values. Invasive critical closing pressure is most sensitive to variations in ICP and CPP and can be used as an indicator of the cerebrospinal and the cerebrovascular system status during infusion tests.
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    Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network (2018)
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    Publication Date: 2018-02-21
    Description: Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.
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    TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection (2018)
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    Publication Date: 2018-02-21
    Description: In this paper, we introduce TR-BREATH, a time-reversal (TR)-based contact-free breathing monitoring system. It is capable of breathing detection and multiperson breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TR-BREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10 s of measurement, a mean accuracy of $99%$ can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of $98.65%$ in breathing rate estimation for a dozen people under the line-of-sight scenario and a mean accuracy of $98.07%$ in breathing rate estimation of nine people under the NLOS scenario, both with 63 s of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.
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    Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea–hypopnea index using nocturnal pulse oximetry. Methods: Among 230 polysomnographic recordings with apnea–hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea–hypopnea index from the apneic event detection results. Results: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea–hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea–hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. Conclusion: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. Significance: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeu- ic intervention and follow-up study on sleep apnea.
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    Former CiSE EICs Reflect on the Magazines 20th Anniversary (2018)
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    Publication Date: 2018-02-17
    Description: Former CiSE EICs reflect on the magazine’s 20th anniversary.
    Print ISSN: 1521-9615
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    Topics: Computer Science , Natural Sciences in General , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The American Institute of Physics (AIP) and the IEEE Computer Society.
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    The Rise of JavaScript (2018)
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    Publication Date: 2018-02-17
    Description: In this special issue of CiSE, we focus on a small sample of libraries and tools written in JavaScript that might interest scientists and engineers. Our goal is, of course, not to be exhaustive nor representative—given the huge and rapidly expanding field—but to spark the readers curiosity and show how certain tasks are remarkably easy in JavaScript.
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    General-Purpose Computation on GPUs in the Browser Using gpu.js (2018)
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    Publication Date: 2018-02-17
    Description: gpu.js is a client-side general-purpose computing on graphics processing units (GPGPU) library for the browser written entirely in JavaScript. Unlike some of the earlier implementations of client-side GPGPU, gpu.js does not require browser support through an explicit GPGPU API or an installation of a custom native runtime browser extension to enable such support. This allows the library to run on all modern platforms, including on mobile devices such as smartphones. It achieves this by using the already widely adopted Web Graphics Library graphics API in a manner that it is not designed for by making use of JavaScript-to-Open GL Shading Language transpilation. The library abstracts away all the unnecessary implementation details of awkwardly performing GPGPU on a graphics API and, at the same time, provides an API that was designed specifically for GPGPU.
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    Toy Vision-Guided 3D Robotic Arm in JavaScript (2018)
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    Publication Date: 2018-02-17
    Description: Modern JavaScript has the power to access the capabilities of the underlying architecture in a simple and portable manner and without installing third-party libraries. A one-page web application was built that connects to the camera, processes the video stream, extracts images, and detects motion. The detected motion is then used to control a 3D robotic arm rendered using webGL within the same page. Although the scope of this paper is purely didactic, the framework can easily be extended and used in a classroom setting for experimentation with different vision algorithms.
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    Fast Physics on the Web Using C++, JavaScript, and Emscripten (2018)
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    Publication Date: 2018-02-17
    Description: The web is now capable of running computationally intensive code at near-native speed, enabling new types of content such as physics simulations to be easily shared online. In this article, the author shows how to do just that by writing code in C++ and compiling it to the web with the open-source Emscripten tool, which generates asm.js, a fast subset of JavaScript that runs in all web browsers.
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    Autonomous Wireless Lake Monitoring (2018)
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    Publication Date: 2018-02-17
    Description: Underwater sensor networks (USNs) are an emerging research field, with applications ranging from military to environment monitoring. The authors present an innovative USN application: autonomous wireless lake monitoring. Such technology can be applied to monitor lakes, especially for limnology, where scientists determine water quality by measuring environmental variables such as temperature, pH, and dissolved oxygen. This application, which can collect and analyze those physical quantities in near real time, could help improve quality of life for humans and prevent ecodisasters.
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    Front Cover (2018)
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    Publication Date: 2018-02-17
    Description: In this special issue of CiSE, we focus on a small sample of libraries and tools written in JavaScript that might interest scientists and engineers. Our goal is to spark the readers curiosity and show how certain tasks are remarkably easy in JavaScript. In particular, we cover the following tasks: compiling C/C++ code to JavaScript using Emscripten; performing symbolic math; programming the GPU; experimenting with a camera and machine vision; and creating complex interfaces.
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    Data-Driven Molecular Engineering of Solar-Powered Windows (2018)
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    Publication Date: 2018-02-17
    Description: A new initiative in data science is using the Mira and Theta supercomputers at the US Department of Energy’s Argonne National Laboratory to discover new dye materials suitable for dye-sensitized solar cells. This project aims to develop a new material-by-design methodology by using natural language processing, machine learning, and data mining in conjunction with large-scale simulation and experiments. This synergistic computational and experimental science approach will enable the discovery of new light-absorbing dye molecules, which are needed for the development of solar-powered windows that have the potential to power buildings in an entirely energy-sustainable fashion.
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    Lexicon Visualization Library and JavaScript for Scientific Data Visualization (2018)
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    Publication Date: 2018-02-17
    Description: It’s becoming increasingly challenging to efficiently visualize and extract useful insight from complex and big data sets. JavaScript stands out as a suitable programming choice that offers mature libraries, easy implementation, and extensive customization, all of which stay in the shadow of new and rapid developments in the language. To illustrate the use of JavaScript in a scientific context, this article elaborates on Lexicon, a collection of JavaScript libraries for generating interactive visualizations in bioinformatics and other custom libraries.
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    Graph-Based Techniques for Visual Analytics of Scientific Data Sets (2018)
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    Publication Date: 2018-02-17
    Description: Transforming generic and powerful visual means into a visual interface for navigating and exploring scientific data sets requires a fully integrated pipeline of data transformation, representation, and visual mapping. This article presents TransGraph for time-varying data visualization and FlowGraph for flow field exploration. Both graph designs are hierarchical, enabling level-of-detail exploration of large scientific data in an adaptive manner.
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    The Consciences of Robot Warriors (2018)
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    Publication Date: 2018-02-17
    Description: It’s not a stretch to compare a soldier’s orders to the program that governs today’s autonomous drones. In those circumstances, the programmer and the person who authorized the program are responsible and culpable in the event of a bug that causes a war crime.
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    TMSCS ad (2018)
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    Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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    IEEE Computer Society Information (2018)
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    Publication Date: 2018-02-17
    Description: Provides a listing of current committee members and society officers.
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    How Do We Create More Equitable, Diverse, and Inclusive Organizations, and Why Does it Matter? A White Male’s Perspective (2018)
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    Publication Date: 2018-02-17
    Description: Diversity and inclusion must be personally meaningful so that they continue to be on our minds in everything we do long after diversity training has ended. When we create deep connections, we build community and inspire others to follow suit, gently but boldly leading a wave of change throughout our organizations. This is how we make computational science more equitable, diverse, and inclusive.
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    Domain-Specific Languages in Scientific Computing (2018)
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    Publication Date: 2018-02-17
    Description: If you have been following developments in software engineering in recent years, you have probably noticed that the term DSL (domain-specific language) has become a minor buzzword in that field. You may have concluded that this is a hot new idea that is certainly not ready for application in real life. But, as I will show in this article, computational scientists (and others) have been using DSLs for decades. What is new is not DSLs per se, but the name and the attention given to them.
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    Table of contents (2018)
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    Description: Presents the table of contents for this issue of this publication.
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    From Experts Guiding Data to Data Guiding Solutions (2018)
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    Description: Data is the prime mover of computational analysis and the central actor in Machine Learning by Ethem Alpaydin, a 2016 book in the MIT Press Essential Knowledge Series.
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    Math.Js: An Advanced Mathematics Library For JavaScript (2018)
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    Publication Date: 2018-02-17
    Description: Math.js is a JavaScript library that brings advanced mathematics to the web browser and server. The case study in this article demonstrates its flexibility by extending the library using custom functions to solve and optimize a rocket trajectory. Several benchmark comparisons with other JavaScript libraries and state-of-the-art mathematics software are presented, and the current challenges facing math.js, including performance and size, are discussed.
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    TSUSC ad (2018)
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    Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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    IEEE Engineering in Medicine and Biology Society (2018)
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    Publication Date: 2018-02-21
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    Frontcover (2018)
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    Description: Presents the front cover for this issue of the publication.
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    Towards Improved Design and Evaluation of Epileptic Seizure Predictors (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics. Methods: We used the competition's datasets to illustrate the effects of data contamination. Having better data partitions, we compared three types of models in terms of different objectives. Results: We found that correct selection of test samples is crucial when evaluating the performance of seizure forecasting models. Moreover, we showed that models, which achieve state-of-the-art performance with respect to commonly used AUC, sensitivity, and specificity metrics, may not yet be suitable for practical usage because of low precision scores. Conclusion: Correlation between validation and test datasets used in the competition limited its scientific value. Significance: Our findings provide guidelines which allow for a more objective evaluation of seizure prediction models.
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    Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Many common eye diseases and cardiovascular diseases can be diagnosed through retinal imaging. However, due to uneven illumination, image blurring, and low contrast, retinal images with poor quality are not useful for diagnosis, especially in automated image analyzing systems. Here, we propose a new image enhancement method to improve color retinal image luminosity and contrast. Methods: A luminance gain matrix, which is obtained by gamma correction of the value channel in the HSV (hue, saturation, and value) color space, is used to enhance the R, G, and B (red, green and blue) channels, respectively. Contrast is then enhanced in the luminosity channel of L * a * b * color space by CLAHE (contrast-limited adaptive histogram equalization). Image enhancement by the proposed method is compared to other methods by evaluating quality scores of the enhanced images. Results: The performance of the method is mainly validated on a dataset of 961 poor-quality retinal images. Quality assessment (range 0–1) of image enhancement of this poor dataset indicated that our method improved color retinal image quality from an average of 0.0404 (standard deviation 0.0291) up to an average of 0.4565 (standard deviation 0.1000). Conclusion: The proposed method is shown to achieve superior image enhancement compared to contrast enhancement in other color spaces or by other related methods, while simultaneously preserving image naturalness. Significance: This method of color retinal image enhancement may be employed to assist ophthalmologists in more efficient screening of retinal diseases and in development of improved automated image analysis for clinical diagnosis.
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    An Interactive Simulator for Imposing Virtual Musculoskeletal Dynamics (2018)
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    Publication Date: 2018-02-21
    Description: Objective: disease processes are often marked by both neural and muscular changes that alter movement control and execution, but these adaptations are difficult to tease apart because they occur simultaneously. This is addressed by swapping an individual's limb dynamics with a neurally controlled facsimile using an interactive musculoskeletal simulator (IMS) that allows controlled modifications of musculoskeletal dynamics. This paper details the design and operation of the IMS, quantifies and describes human adaptation to the IMS, and determines whether the IMS allows users to move naturally, a prerequisite for manipulation experiments. Methods: healthy volunteers ( n = 4) practiced a swift goal-directed task (back-and-forth elbow flexion/extension) for 90 trials with the IMS off (normal dynamics) and 240 trials with the IMS on, i.e., the actions of a user's personalized electromyography-driven musculoskeletal model are robotically imposed back onto the user. Results: after practicing with the IMS on, subjects could complete the task with end-point errors of 1.56°, close to the speed-matched IMS-off error of 0.57°. Muscle activity, joint torque, and arm kinematics for IMS-on and -off conditions were well matched for three subjects (root-mean-squared error [RMSE] = 0.16 N·m), but the error was higher for one subject with a small stature (RMSE = 0.25 N·m). Conclusion: a well-matched musculoskeletal model allowed IMS users to perform a goal-directed task nearly as well as when the IMS was not active. Significance: this advancement permits real-time manipulations of musculoskeletal dynamics, which could increase our understanding of muscular and- neural co-adaptations to injury, disease, disuse, and aging.
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    The ICVSIE: A General Purpose Integral Equation Method for Bio-Electromagnetic Analysis (2018)
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    Publication Date: 2018-02-21
    Description: Objective: An internally combined volume surface integral equation (ICVSIE) for analyzing electromagnetic (EM) interactions with biological tissue and wide ranging diagnostic, therapeutic, and research applications, is proposed. Method: The ICVSIE is a system of integral equations in terms of volume and surface equivalent currents in biological tissue subject to fields produced by externally or internally positioned devices. The system is created by using equivalence principles and solved numerically; the resulting current values are used to evaluate scattered and total electric fields, specific absorption rates, and related quantities. Results: The validity, applicability, and efficiency of the ICVSIE are demonstrated by EM analysis of transcranial magnetic stimulation, magnetic resonance imaging, and neuromuscular electrical stimulation. Conclusion: Unlike previous integral equations, the ICVSIE is stable regardless of the electric permittivities of the tissue or frequency of operation, providing an application-agnostic computational framework for EM-biomedical analysis. Significance: Use of the general purpose and robust ICVSIE permits streamlining the development, deployment, and safety analysis of EM-biomedical technologies.
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    Toward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results (2018)
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    Publication Date: 2018-02-21
    Description: Objective : Contemporary and future outpatient long-term artificial pancreas (AP) studies need to cope with the well-known large intra- and interday glucose variability occurring in type 1 diabetic (T1D) subjects. Here, we propose an adaptive model predictive control (MPC) strategy to account for it and test it in silico. Methods : A run-to-run (R2R) approach adapts the subcutaneous basal insulin delivery during the night and the carbohydrate-to-insulin ratio (CR) during the day, based on some performance indices calculated from subcutaneous continuous glucose sensor data. In particular, R2R aims, first, to reduce the percentage of time in hypoglycemia and, secondarily, to improve the percentage of time in euglycemia and average glucose. In silico simulations are performed by using the University of Virginia/Padova T1D simulator enriched by incorporating three novel features: intra- and interday variability of insulin sensitivity, different distributions of CR at breakfast, lunch, and dinner, and dawn phenomenon. Results : After about two months, using the R2R approach with a scenario characterized by a random $pm$ 30% variation of the nominal insulin sensitivity the time in range and the time in tight range are increased by 11.39% and 44.87%, respectively, and the time spent above 180 mg/dl is reduced by 48.74%. Conclusions : An adaptive MPC algorithm based on R2R shows in silico great potential to capture intra- and interday glucose variability by improving both overnight and postprandial glucose control without increasing hypoglycemia. Significance : Making an AP adaptive is key for long-term real-life outpatient studies. These good in silico results are very encouraging and worth testing in vivo .
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    Real-Time Error Control for Surgical Simulation (2018)
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    Publication Date: 2018-02-21
    Description: Objective : To present the first a posteriori error-driven adaptive finite element approach for real-time simulation, and to demonstrate the method on a needle insertion problem. Methods : We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. 1 For simulating soft tissue deformation, the refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local $h$ -refinement. Results : We control the local and global error level in the mechanical fields (e.g., displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force–displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach. Conclusions : Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations. Significance : Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.
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    Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems (2018)
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    Publication Date: 2018-02-21
    Description: Objective: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose. Methods: Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator. Results: The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70–180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70–180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC. Conclusion: A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations. Significance: Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.
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    Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc. Detection of these lesions plays a significant role for early diagnosis of DR. Methods: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing. The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter. The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels. Results and Conclusions: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of $97.71%$ and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.
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    Reduction of Blood Glucose Measurements to Calibrate Subcutaneous Glucose Sensors: A Bayesian Multiday Framework (2018)
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    Publication Date: 2018-02-21
    Description: Objective: In most continuous glucose monitoring (CGM) devices used for diabetes management, the electrical signal measured by the sensor is transformed to glucose concentration by a calibration function whose parameters are estimated using self-monitoring of blood glucose (SMBG) samples. The calibration function is usually a linear model approximating the nonlinear relationship between electrical signal and glucose concentration in certain time intervals. Thus, CGM devices require frequent calibrations, usually twice a day. The aim here is to develop a new method able to reduce the frequency of calibrations. Methods : The algorithm is based on a multiple-day model of sensor time-variability with second-order statistical priors on its unknown parameters. In an online setting, these parameters are numerically determined by the Bayesian estimation exploiting SMBG sparsely collected by the patient. The method is assessed retrospectively on 108 CGM signals acquired for 7 days by the Dexcom G4 Platinum sensor, testing progressively less-calibration scenarios. Results : Despite the reduction of calibration frequency (on average from 2/day to 0.25/day), the method shows a statistically significant accuracy improvement compared to manufacturer calibration, e.g., mean absolute relative difference when compared to a laboratory reference decreases from 12.83% to 11.62% ( p -value of 0.006). Conclusion : The methodology maintains (sometimes improves) CGM sensor accuracy compared to that of the original manufacturer, while reducing the frequency of calibrations. Significance : Reducing the need of calibrations facilitates the adoption of CGM technology both in terms of ease of use and cost, an obvious prerequisite for its use as replacement of traditional SMBG devices.
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    Real-Time Three-Dimensional Microwave Monitoring of Interstitial Thermal Therapy (2018)
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    Publication Date: 2018-02-21
    Description: We report a method for real-time three-dimensional monitoring of thermal therapy through the use of noncontact microwave imaging. This method is predicated on using microwaves to image changes in the dielectric properties of tissue with changing temperature. Instead of the precomputed linear Born approximation that was used in prior work to speed up the frame-to-frame inversions, here we use the nonlinear distorted Born iterative method (DBIM) to solve the electric volume integral equation (VIE) to image the temperature change. This is made possible by using a recently developed graphic processing unit accelerated conformal finite difference time domain method to solve the forward problem and update the electric field in the monitored region in each DBIM iteration. Compared to our previous work, this approach provides a far superior approximation of the electric field within the VIE, and thus yields a more accurate reconstruction of tissue temperature change. The proposed method is validated using a realistic numerical model of interstitial thermal therapy for a deep-seated brain lesion. With the new DBIM, we reduced the average estimation error of the mean temperature within the region of interest from 2.5 $^circ$ to 1.0 $^circ$ for the noise-free case, and from 2.9 $^circ$ to 1.7 $^circ$ for the 2% background noise case.
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    Table of Contents (2018)
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    Publication Date: 2018-02-21
    Description: Presents the table of contents for this issue of the publication.
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    Post-Ventricular Premature Contraction Phase Correction Improves the Predictive Value of Average T-Wave Alternans in Ambulatory ECG Recordings (2018)
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    Publication Date: 2018-02-21
    Description: Objective : We proposed and evaluated a method for correcting possible phase shifts provoked by the presence of ventricular premature contractions (VPCs) for a better assessment of T-wave alternans (TWA).  Methods: First, we synthesized ECG signals with artificial TWA in the presence of different noise sources. Then, we assessed the prognostic value for sudden cardiac death (SCD) of the long-term average of TWA amplitude (the index of average alternans, $IAA$ ) in ambulatory ECG signals from congestive heart failure (CHF) and evaluated whether it is sensitive to the presence of VPCs. Results: The inclusion of the phase correction after VPC in the processing always improved estimation accuracy of the $IAA$ under different noisy conditions and regardless of the number of the VPCs included in the sequence. It also presented a positive impact on the prognostic value of $IAA$ with increased hazard ratios (from 17% to 29%, depending of the scenario) in comparison to the noninclusion of this step. Conclusion: The proposed methodology for $IAA$ estimation, which corrects for the possible phase reversal on TWA after the presence of VPCs, represents a robust TWA estimation approach with a significant impact on the prognostic value of $IAA$ for SCD stratification in CHF patients. Significance: An accurate TWA estimation has a potential direct clinical impact on noninvasive SCD stratification, allowing better identification of patients at higher risk and helping clinicians in adopting the - ost appropriate therapeutic strategy.
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    A Flexure-Guided Piezo Drill for Penetrating the Zona Pellucida of Mammalian Oocytes (2018)
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    Publication Date: 2018-02-21
    Description: Mammalian oocytes such as mouse oocytes have a highly elastic outer membrane, zona pellucida (ZP) that cannot be penetrated without significantly deforming the oocyte, even with a sharp micropipette. Piezo drill devices leverage lateral and axial vibration of the micropipette to accomplish ZP penetration with greatly reduced oocyte deformation. However, existing piezo drills all rely on a large lateral micropipette vibration amplitude ( $>$ 20 $mu {rm m}$ ) and a small axial vibration amplitude ( $〈$ 0.1 $mu {rm m}$ ). The very large lateral vibration amplitude has been deemed to be necessary for ZP penetration although it also induces larger oocyte deformation and more oocyte damage. This paper reports on a new piezo drill device that uses a flexure guidance mechanism and a systematically designed pulse train with an appropriate base frequency. Both simulation and experimental results demonstrate that a small lateral vibration amplitude (e.g., 2 $mu {rm m}$ ) and an axial vibration amplitude as large as 1.2 $mu {rm m}$ were achieved. Besides achieving 100% effectiveness in the penetration of mouse oocytes ( n = 45), the new piezo device during ZP penetration induced a small oocyte deformation of 3.4 $mu {rm m}$ versus larger than 10 $mu {rm m}$ using existing piezo drill devices.
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    Mechanical Imaging of Soft Tissues With a Highly Compliant Tactile Sensing Array (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Objective : The mechanical imaging of lumps in tissues via surface measurements can permit the noninvasive detection of disease-related differences in body tissues. We present and evaluate sensing techniques for the mechanical imaging of soft tissues, using a highly compliant electronic sensing array. Methods : We developed a mechanical imaging system for capturing tissue properties during automatic- or human-guided palpation. It combines extremely compliant capacitive tactile sensors based on soft polymers and microfluidic electrodes with custom electronic data acquisition hardware, and new algorithms for enhanced tactile imaging by reference to nominal tissue responses. Results : We demonstrate that the system is able to image simulated tumors (lumps), yielding accurate estimates of cross-sectional area independent of embedding depth. In addition, as a proof of concept, we show that similar tactile images can be obtained when the sensor is worn on a palpating finger. Conclusion : Soft capacitive sensors can accurately image lumps in soft tissue provided that care is taken to control and compensate for electrical and mechanical background signals. Significance : The results underline the utility of soft electronic sensors for applications in medical imaging or clinical practices of palpation.
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    ResCSRF: Algorithm to Automatically Extract Cheyne–Stokes Respiration Features From Respiratory Signals (2018)
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    Publication Date: 2018-02-21
    Description: Objective : Cheyne–Stokes respiration (CSR) related features are significantly associated with cardiac dysfunction. Scoring of these features is labor intensive and time-consuming. To automate the scoring process, an algorithm (ResCSRF) has been developed to extract these features from nocturnal measurement of respiratory signals. Methods : ResCSRF takes four signals (nasal flow, thorax, abdomen, and finger oxygen saturation) as input. It first detects CSR cycles and then calculates the respiratory features (cycle length, lung-to-periphery circulation time, and time to peak flow). It outputs nightly statistics (mean, median, standard deviation, and percentiles) of these features. It was developed and blindly tested on a group of 49 chronic heart failure patients undergoing overnight in-home unattended respiratory polygraphy recordings. Results: The performance of ResCSRF was evaluated against manual expert scoring (ES) (consensus between two independent sleep scorers). In terms of percentage of CSR per recording, the mean difference [reproducibility coefficient (RPC)] between ResCSRF and ES was $-$ 0.5(6.4) and 0.5(8.1) for development and test set, respectively. The nightly statistics of CSR-related features output by ResCSRF showed high correlation with ES on the blind test set with the mean difference of less than 3 s and RPC of less than 7 s. Conclusions: These results indicate that ResCSRF is capable of automating the scoring of CSR-related features and could potentially be implemented into a remote monitoring system to regularly monitor patients’ cardiac function.
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    Noninvasive In-Situ Measurement of Blood Lactate Using Microwave Sensors (2018)
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    Publication Date: 2018-02-21
    Description: Goal: This paper reports a novel electromagnetic sensor technique for real-time noninvasive monitoring of blood lactate in human subjects. Methods: The technique was demonstrated on 34 participants who undertook a cycling regime, with rest period before and after, to produce a rising and falling lactate response curve. Sensors attached to the arm and legs of participants gathered spectral data, blood samples were measured using a Lactate Pro V2; temperature and heart rate data was also collected. Results: Pointwise mutual information and neural networks are used to produce a predictive model. The model shows a good correlation ${rm{(R,= ,0.78)}}$ between the standard invasive and novel noninvasive electromagnetic wave based blood lactate measurements, with an error of 13.4% in the range of 0–12 mmol/L. Conclusion: The work demonstrates that electromagnetic wave sensors are capable of determining blood lactate level without the need for invasive blood sampling. Significance: Measurement of blood metabolites, such as blood lactate, in real-time and noninvasively in hospital environments will reduce the risk of infection, increase the frequency of measurement and ensure timely intervention only when necessary. In sports, such tools will enhance training of athletes, and enable more effecting training regimes to be prescribed.
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    Effect of Tissue Heterogeneity on the Transmembrane Potential of Type-1 Spiral Ganglion Neurons: A Simulation Study (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-21
    Description: Electric stimulation of the auditory nerve by cochlear implants has been a successful clinical intervention to treat the sensory neural deafness. In this pathological condition of the cochlea, type-1 spiral ganglion neurons in Rosenthal's canal play a vital role in the action potential initiation. Various morphological studies of the human temporal bones suggest that the spiral ganglion neurons are surrounded by heterogeneous structures formed by a variety of cells and tissues. However, the existing simulation models have not considered the tissue heterogeneity in the Rosenthal's canal while studying the electric field interaction with spiral ganglion neurons. Unlike the existing models, we have implemented the tissue heterogeneity in the Rosenthal's canal using a computationally inexpensive image based method in a two-dimensional finite element model. Our simulation results suggest that the spatial heterogeneity of surrounding tissues influences the electric field distribution in the Rosenthal's canal, and thereby alters the transmembrane potential of the spiral ganglion neurons. In addition to the academic interest, these results are especially useful to understand how the latest tissue regeneration methods such as gene therapy and drug-induced resprouting of peripheral axons, which probably modify the density of the tissues in the Rosenthal's canal, affect the cochlear implant functionality.
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    Semiautomatic Radiofrequency Ablation Planning Based on Constrained Clustering Process for Hepatic Tumors (2018)
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    Publication Date: 2018-02-21
    Description: Radiofrequency ablation (RFA) is currently one of the most effective methods for minimally invasive treatment of hepatic tumors. Planning the probe placements is an essential and challenging step in RFA treatment. To completely destroy the tumor with minimum amount of affected native tissue, a new RFA planning system is proposed in this paper. In the proposed planning system, the minimum number of ablations and a conical insertion region for each ablation session are determined automatically. Based on the geometric character of the tumor, a novel clustering algorithm is developed to allow a better layout of the overlapping ablations. For each case, we force the clustering process under the constraint of a manually defined puncture scope , such that all of the needle trajectories are gathered in a reasonable region. Moreover, the proposed planning system enables the clinician to manually choose a proper insertion path inside the conical insertion region to avoid penetrating large vessels or ribs, which is critical in RFA treatment. The proposed planning system was evaluated on 18 CT scan images and two clinical cases. Results implied that the planning system could provide feasible and accurate RFA treatment plans for hepatic tumors.
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    Novel Modeling of Somatosensory Evoked Potentials for the Assessment of Spinal Cord Injury (2018)
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    Publication Date: 2018-02-21
    Description: Objective: Previous work has shown that differences in the somatosensory evoked potential (SEP) signals between a normal spinal pathway and spinal pathway affected by spinal cord injury (SCI) provide a means to study the degree of injury. This paper proposes a novel quantitative SCI assessment method using time-domain SEP signals. Methods: A pruned and unstructured fit between SEP signals from a normal spinal pathway and a spinal pathway affected by SCI is developed using methods inspired by recent results in sparse reconstruction theory. The coefficients from the resulting fit are used to develop a quantitative assessment of SCI that is tested on actual SEP signals collected from rodents that have been subjected to partial and complete spinal cord transection. Results: The proposed method provides a rich parametric measure that integrates SEP amplitude, time latency, and morphology, while exhibiting a high degree of correlation with existing subjective and quantitative SCI assessment methods. Conclusion: The proposed SCI encapsulates a model of the injury to quantify SCI. Significance: The proposed SCI quantification method may be used to complement existing SCI assessment methods.
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    IEEE Transactions on Biomedical Engineering Handling Editors (2018)
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    Publication Date: 2018-02-21
    Description: Presents a listing of the Handling Editors for this issue of the publication.
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    IEEE Transactions on Biomedical Engineering (T-BME) (2018)
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    IEEE Transactions on Medical Imaging publication information (2018)
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    Publication Date: 2018-01-03
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    An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy (2018)
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    Publication Date: 2018-01-03
    Description: In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.
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    Skeletal Shape Correspondence Through Entropy (2018)
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    Publication Date: 2018-01-03
    Description: We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object’s interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes’ geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al. , entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes’ geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.
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    Post Hoc Analysis of Passive Cavitation Imaging for Classification of Histotripsy-Induced Liquefaction in Vitro (2018)
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    Publication Date: 2018-01-03
    Description: Histotripsy utilizes focused ultrasound to generate bubble clouds for transcutaneous tissue liquefaction. Bubble activity maps are under development to provide image guidance and monitor treatment progress. The aim of this paper was to investigate the feasibility of using plane wave B-mode and passive cavitation images to be used as binary classifiers of histotripsy-induced liquefaction. Prostate tissue phantoms were exposed to histotripsy pulses over a range of pulse durations (5– $20~mu text{s}$ ) and peak negative pressures (12–23 MPa). Acoustic emissions were recorded during the insonation and beamformed to form passive cavitation images. Plane wave B-mode images were acquired following the insonation to detect the hyperechoic bubble cloud. Phantom samples were sectioned and stained to delineate the liquefaction zone. Correlation between passive cavitation and plane wave B-mode images and the liquefaction zone was assessed using receiver operating characteristic (ROC) curve analysis. Liquefaction of the phantom was observed for all the insonation conditions. The area under the ROC (0.94 versus 0.82), accuracy (0.90 versus 0.83), and sensitivity (0.81 versus 0.49) was greater for passive cavitation images relative to B-mode images ( ${p} 〈 0.05$ ) along the azimuth of the liquefaction zone. The specificity was greater than 0.9 for both imaging modalities. These results demonstrate a stronger correlation between histotripsy-induced liquefaction and passive cavitation imaging compared with the plane wave B-mode imaging, albeit with limited passive cavitation image range resolution.
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    A Direct Algorithm for Optimization Problems With the Huber Penalty (2018)
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    Description: We present a direct (noniterative) algorithm for 1-D quadratic data fitting with neighboring intensity differences penalized by the Huber function. Applications of such an algorithm include 1-D processing of medical signals, such as smoothing of tissue time concentration curves in kinetic data analysis or sinogram preprocessing, and using it as a subproblem solver for 2-D or 3-D image restoration and reconstruction. dynamic programming was used to develop the direct algorithm. The problem was reformulated as a sequence of univariate optimization problems, for ${k} =textsf {1}, ldots , {N}$ , where ${N}$ is the number of data points. The solution to the univariate problem at index ${k}$ is parameterized by the solution at ${k}+textsf {1}$ , except at ${k}={N}$ . Solving the univariate optimization problem at ${k}={N}$ yields the solution to each problem in the sequence using back-tracking. Computational issues and memory cost are discussed in detail. Two numerical studies, tissue concentration curve smoothing and sinogram preprocessing for image reconstruction, are used to validate the direct algorithm and illustrate its practical applications. In the example of 1-D curve smoothing, the efficiency of the direct algorithm is compared with four iterative methods: the iterative coordinate descent, Nesterov’s accelerated gradient descent algorithm, FISTA, and an off-the-shelf second order method. The first two methods were applied to the primal problem, the others to the dual problem. The comparisons show that the direct - lgorithm outperforms all other methods by a significant factor, which rapidly grows with the curvature of the Huber function. The second example, sinogram preprocessing, showed that robustness and speed of the direct algorithm are maintained over a wide range of signal variations, and that noise and streaking artifacts could be reduced with almost no increase in computation time. We also outline how the proposed 1-D solver can be used for imaging applications.
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    Significant Anatomy Detection Through Sparse Classification: A Comparative Study (2018)
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    Description: We present a comparative study for discriminative anatomy detection in high dimensional neuroimaging data. While most studies solve this problem using mass univariate approaches, recent works show better accuracy and variable selection using a sparse classification model. Two types of image-based regularization methods have been proposed in the literature based on either a Graph Net (GN) model or a total variation (TV) model. These studies showed increased classification accuracy and interpretability of results when using image-based regularization, but did not look at the accuracy and quality of the recovered significant regions. In this paper, we theoretically prove bounds on the recovered sparse coefficients and the corresponding selected image regions in four models (two based on GN penalty and two based on TV penalty). Practically, we confirm the theoretical findings by measuring the accuracy of selected regions compared with ground truth on simulated data. We also evaluate the stability of recovered regions over cross-validation folds using real MRI data. Our findings show that the TV penalty is superior to the GN model. In addition, we showed that adding an l 2 penalty improves the accuracy of estimated coefficients and selected significant regions for the both types of models.
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    Image Registration Based on Low Rank Matrix: Rank-Regularized SSD (2018)
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    Description: Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based on singular values of difference image in mono-modal imaging. In fact, image registration and distortion correction are performed simultaneously in the proposed model. Based on our experiments, the RRSSD similarity measure achieves clinically acceptable registration results, and outperforms other state-of-the-art similarity measures, such as the well-known method of residual complexity.
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    Structure Prediction for Gland Segmentation With Hand-Crafted and Deep Convolutional Features (2018)
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    Publication Date: 2018-01-03
    Description: We present a novel method to segment instances of glandular structures from colon histopathology images. We use a structure learning approach which represents local spatial configurations of class labels, capturing structural information normally ignored by sliding-window methods. This allows us to reveal different spatial structures of pixel labels (e.g., locations between adjacent glands, or far from glands), and to identify correctly neighboring glandular structures as separate instances. Exemplars of label structures are obtained via clustering and used to train support vector machine classifiers. The label structures predicted are then combined and post-processed to obtain segmentation maps. We combine hand-crafted, multi-scale image features with features computed by a deep convolutional network trained to map images to segmentation maps. We evaluate the proposed method on the public domain GlaS data set, which allows extensive comparisons with recent, alternative methods. Using the GlaS contest protocol, our method achieves the overall best performance.
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    Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections (2018)
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    Publication Date: 2018-01-03
    Description: Accurate identification of the needle target is crucial for effective epidural anesthesia. Currently, epidural needle placement is administered by a manual technique, relying on the sense of feel, which has a significant failure rate. Moreover, misleading the needle may lead to inadequate anesthesia, post dural puncture headaches, and other potential complications. Ultrasound offers guidance to the physician for identification of the needle target, but accurate interpretation and localization remain challenges. A hybrid machine learning system is proposed to automatically localize the needle target for epidural needle placement in ultrasound images of the spine. In particular, a deep network architecture along with a feature augmentation technique is proposed for automatic identification of the anatomical landmarks of the epidural space in ultrasound images. Experimental results of the target localization on planes of 3-D as well as 2-D images have been compared against an expert sonographer. When compared with the expert annotations, the average lateral and vertical errors on the planes of 3-D test data were 1 and 0.4 mm, respectively. On 2-D test data set, an average lateral error of 1.7 mm and vertical error of 0.8 mm were acquired.
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    Table of contents (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-03
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    Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance Models (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-03
    Description: The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The DNSM’s parametric nature allows the use of powerful local prior statistics, and its implicit nature removes the need to use landmarks and easily handles topological changes. In a Bayesian inference framework, we model arbitrary shape and appearance distributions using nonparametric density estimations, at any local scale. The proposed local shape prior results in accurate segmentation even when very few training shapes are available, because the method generates a rich set of shape variations by locally combining training samples. We demonstrate the performance of the framework by applying it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.
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    Automated Registration of 3-D Knee Implant Models to Fluoroscopic Images Using Lipschitzian Optimization (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-03
    Description: This paper describes an automated method for registering 3-D models of metallic knee implants to single-plane radiographic images. We develop a multistage approach that identifies the correct pose by matching altered dilations of an edge-detected image with the silhouette of an implant model. The location of the similarity function’s minimum is found using a novel optimization routine that combines the Dividing Rectangles algorithm with properties of the registration metric. Depending on the implant type (tibial or femoral), this technique reliably converges under maximum displacements of approximately 25 to 55 millimeters for translation components and 25° to 55° for Euler angles. The method proves to be robust to noise from bones and soft tissue. After an initial guess for the first image in the sequence, subsequent frames can be automatically registered from the optimum pose in the previous image.
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    IEEE Transactions on Medical Imaging information for authors (2018)
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    Publication Date: 2018-01-03
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  • 96
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    Optimization of Coil Element Configurations for a Matrix Gradient Coil (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-03
    Description: Recently, matrix gradient coils (also termed multi-coils or multi-coil arrays) were introduced for imaging and B 0 shimming with 24, 48, and even 84 coil elements. However, in imaging applications, providing one amplifier per coil element is not always feasible due to high cost and technical complexity. In this simulation study, we show that an 84-channel matrix gradient coil (head insert for brain imaging) is able to create a wide variety of field shapes even if the number of amplifiers is reduced. An optimization algorithm was implemented that obtains groups of coil elements, such that a desired target field can be created by driving each group with an amplifier. This limits the number of amplifiers to the number of coil element groups. Simulated annealing is used due to the NP-hard combinatorial nature of the given problem. A spherical harmonic basis set up to the full third order within a sphere of 20-cm diameter in the center of the coil was investigated as target fields. We show that the median normalized least squares error for all target fields is below approximately 5% for 12 or more amplifiers. At the same time, the dissipated power stays within reasonable limits. With a relatively small set of amplifiers, switches can be used to sequentially generate spherical harmonics up to third order. The costs associated with a matrix gradient coil can be lowered, which increases the practical utility of matrix gradient coils.
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    Improved Sensitivity in Ultrasound Molecular Imaging With Coherence-Based Beamforming (2018)
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    Publication Date: 2018-01-03
    Description: Ultrasound molecular imaging (USMI) is accomplished by detecting microbubble (MB) contrast agents that have bound to specific biomarkers, and can be used for a variety of imaging applications, such as the early detection of cancer. USMI has been widely utilized in preclinical imaging in mice; however, USMI in humans can be challenging because of the low concentration of bound MBs and the signal degradation caused by the presence of heterogenous soft tissue between the transducer and the lesion. Short-lag spatial coherence (SLSC) beamforming has been proposed as a robust technique that is less affected by poor signal quality than standard delay-and-sum (DAS) beamforming. In this paper, USMI performance was assessed using contrast-enhanced ultrasound imaging combined with DAS (conventional CEUS) and with SLSC (SLSC-CEUS). Each method was characterized by flow channel phantom experiments. In a USMI-mimicking phantom, SLSC-CEUS was found to be more robust to high levels of additive thermal noise than DAS, with a 6dB SNR improvement when the thermal noise level was +6dB or higher. However, SLSC-CEUS was also found to be insensitive to increases in MB concentration, making it a poor choice for perfusion imaging. USMI performance was also measured in vivo using VEGFR2-targeted MBs in mice with subcutaneous human hepatocellular carcinoma tumors, with clinical imaging conditions mimicked using a porcine tissue layer between the tumor and the transducer. SLSC-CEUS improved the SNR in each of ten tumors by an average of 41%, corresponding to 3.0dB SNR. These results indicate that the SLSC beamformer is well-suited for USMI applications because of its high sensitivity and robust properties under challenging imaging conditions.
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    Motion Tracking of the Carotid Artery Wall From Ultrasound Image Sequences: a Nonlinear State-Space Approach (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-03
    Description: The motion of the common carotid artery (CCA) wall has been established to be useful in early diagnosis of atherosclerotic disease. However, tracking the CCA wall motion from ultrasound images remains a challenging task. In this paper, a nonlinear state-space approach has been developed to track CCA wall motion from ultrasound sequences. In this approach, a nonlinear state-space equation with a time-variant control signal was constructed from a mathematical model of the dynamics of the CCA wall. Then, the unscented Kalman filter (UKF) was adopted to solve the nonlinear state transfer function in order to evolve the state of the target tissue, which involves estimation of the motion trajectory of the CCA wall from noisy ultrasound images. The performance of this approach has been validated on 30 simulated ultrasound sequences and a real ultrasound dataset of 103 subjects by comparing the motion tracking results obtained in this study to those of three state-of-the-art methods and of the manual tracing method performed by two experienced ultrasound physicians. The experimental results demonstrated that the proposed approach is highly correlated with (intra-class correlation coefficient ≥ 0.9948 for the longitudinal motion and ≥ 0.9966 for the radial motion) and well agrees (the 95% confidence interval width is 0.8871 mm for the longitudinal motion and 0.4159 mm for the radial motion) with the manual tracing method on real data and also exhibits high accuracy on simulated data (0.1161 ~ 0.1260 mm). These results appear to demonstrate the effectiveness of the proposed approach for motion tracking of the CCA wall.
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    Parametric Dependencies of Sliding Window Correlation (2018)
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    Publication Date: 2018-01-20
    Description: Objective: In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals. Methods: We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data. Results: For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length. Conclusion: In the absence of any “ground truth” for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC. Significance: This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth.
    Print ISSN: 0018-9294
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    Topics: Medicine , Technology
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    Enhancement of Intercellular Electrical Synchronization by Conductive Materials in Cardiac Tissue Engineering (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-01-20
    Description: Objective : cardiac tissue regeneration for the treatment of cardiovascular diseases has been of great research interest. Under the hypothesis that electrical synchronization of cardiac cells can be aided by conductive materials, electrically conductive scaffolds have been frequently used to improve cardiac tissue regeneration. However, theoretical analysis is presently absent in examining the underlying mechanism and rationally guiding the design of these conductive scaffolds. Methods : here, equivalent-circuit models are proposed, in which two adjacent groups of cardiomyocytes are grown either on a bulk conductive substrate or around conductive nanostructures. When one group of cells leads with action potentials, the membrane depolarization of the following group is investigated. Results : this study reveals that membrane depolarization of the following group is most sensitive to seal resistance to the substrate while surface roughness and conductivity of the material have less influence. In addition, it is found that a multiple-cell group is easier to be depolarized by its adjacent beating cardiomyocytes. For nanostructure-bridged cardiac cells, substantial depolarization occurs only with a seal resistance larger than 10 13  Ω/sqr, which is contradictory to many reported estimations. Conclusion : this work theoretically confirms the positive role of conductive scaffolds and nanostructures in aiding electrical synchronization of cardiac cells and reveals that its performance mainly relies on the cell-device interface. Significance : this work provides a theoretical basis for the rational design of electroactive scaffolds for enhanced cardiac tissue engineering.
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