Grand Challenges at the Interface of Engineering and Medicine

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Feasibility of Actigraphy for Evaluating Sleep and Daytime Physical Activity in Children with Autism Spectrum Disorder

This research evaluated the feasibility of actigraphy to measure sleep and physical activity in children (ages 2-8 years) with autism spectrum disorder (ASD). We also explored associations between sleep and physical activity. Validated screening measures established eligibility. Questionnaires, diaries, and 5 days and 5 nights of actigraphy monitoring were used to collect data. Of the 32 children enrolled, 27 (84.4%) completed actigraphy monitoring. Based on the median steps per day, children with high physical activity had lower total sleep time and more disruptive behaviors than children with low physical activity. Findings support the feasibility of using actigraphy to measure sleep and physical activity in children with ASD. Larger studies are needed to evaluate interactions of physical activity on sleep in this population.

Qualitative Exploration Toward the Development of a Parent-Rated Scale for Insomnia in Children with Autism Spectrum Disorder

Toward the development of a new parent-rating for insomnia, this multi-site qualitative study explored sleep problems and related impacts in children with autism spectrum disorder (ASD) and their families. To ensure content validity of the measure, we conducted six focus groups with caregivers (N = 25) of 24 children (age 3 to 18 years) with ASD. Based on parent report, all children had a history of mild or greater insomnia. The focus group transcripts were systematically coded to identify major themes. Verbatim comments from caretakers were used to generate 134 candidate items. Further review by the research team and an expert panel followed by individual cognitive interviews with 12 parents reduced the item bank to 40. The thematic analysis of focus group transcripts identified 7 categories: (1) Trouble falling asleep; (2) trouble staying asleep; (3) early morning waking; (4) bedtime routines; (5) parental strategies for bedtime management; (6) impact of sleep problems on the child; and (7) impact of sleep problems on the family. The Flesch Kincaid Grade Level of the 40-item version was 7.2 (seventh grade reading level). Insomnia in children with ASD shares features in common with insomnia in the general pediatric population. However, perhaps owing to autistic features such as insistence on sameness, sensory sensitivities, communication impairments, insomnia in children with ASD appears to have unique behavioral manifestations. Content validity and item clarity of the 40-item bank were supported by expert panel review and cognitive interviews with caregivers of children with ASD.

Comparison of Prospective and Retrospective Gated 4D Flow Cardiac MR Image Acquisitions in the Carotid Bifurcation

PURPOSE

To evaluate the agreement of 4D flow cMRI-derived bulk flow features and fluid (blood) velocities in the carotid bifurcation using prospective and retrospective gating techniques.

METHODS

Prospective and retrospective ECG-gated three-dimensional (3D) cine phase-contrast cardiac MRI with three-direction velocity encoding (i.e., 4D flow cMRI) data were acquired in ten carotid bifurcations from men (n = 3) and women (n = 2) that were cardiovascular disease-free. MRI sequence parameters were held constant across all scans except temporal resolution values differed. Velocity data were extracted from the fluid domain and evaluated across the entire volume or at defined anatomic planes (common, internal, external carotid arteries). Qualitative agreement between gating techniques was performed by visualizing flow streamlines and topographical images, and statistical comparisons between gating techniques were performed across the fluid volume and defined anatomic regions.

RESULTS

Agreement in the kinematic data (e.g., bulk flow features and velocity data) were observed in the prospectively and retrospectively gated acquisitions. Voxel differences in time-averaged, peak systolic, and diastolic-averaged velocity magnitudes between gating techniques across all volunteers were 2.7%, 1.2%, and 6.4%, respectively. No significant differences in velocity magnitudes or components ([Formula: see text], [Formula: see text], [Formula: see text]) were observed. Importantly, retrospective acquisitions captured increased retrograde flow in the internal carotid artery (i.e., carotid sinus) compared to prospective acquisitions (10.4 ± 6.3% vs. 4.6 ± 5.3%; [Formula: see text] < 0.05).

CONCLUSION

Prospective and retrospective ECG-gated 4D flow cMRI acquisitions provide comparable evaluations of fluid velocities, including velocity vector components, in the carotid bifurcation. However, the increased temporal coverage of retrospective acquisitions depicts increased retrograde flow patterns (i.e., disturbed flow) not captured by the prospective gating technique.

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. In this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

Quantifying and Visualizing Uncertainty for Source Localization in Electrocardiographic Imaging

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualize associated measurement and modeling errors. In this paper, we study source localization uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localization model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualization techniques, including confidence maps, level-sets, and topology-based visualizations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

A Review of Three-Dimensional Medical Image Visualization

Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers.Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links.Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.

Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps

Morse complexes are gradient-based topological descriptors with close connections to Morse theory. They are widely applicable in scientific visualization as they serve as important abstractions for gaining insights into the topology of scalar fields. Data uncertainty inherent to scalar fields due to randomness in their acquisition and processing, however, limits our understanding of Morse complexes as structural abstractions. We, therefore, explore uncertainty visualization of an ensemble of 2D Morse complexes that arises from scalar fields coupled with data uncertainty. We propose several statistical summary maps as new entities for quantifying structural variations and visualizing positional uncertainties of Morse complexes in ensembles. Specifically, we introduce three types of statistical summary maps - the probabilistic map, the significance map, and the survival map - to characterize the uncertain behaviors of gradient flows. We demonstrate the utility of our proposed approach using wind, flow, and ocean eddy simulation datasets.

Data-Driven Space-Filling Curves

Abstract-We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

Direct Volume Rendering with Nonparametric Models of Uncertainty

We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets.

CPU Ray Tracing of Tree-Based Adaptive Mesh Refinement Data

Adaptive mesh refinement (AMR) techniques allow for representing a simulation's computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high-performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree-based AMR (TB-AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact-free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.

Photographic High-Dynamic-Range Scalar Visualization

We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features, based on a systematic evaluation, and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.

Perceptually guided contrast enhancement based on viewing distance

We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.

Drift-kelp suppresses foraging movement of overgrazing sea urchins

Sea urchins have the capacity to destructively overgraze kelp beds and cause a wholesale shift to an alternative and stable 'urchin barren' reef state. However, destructive grazing appears labile and contingent on behavioural shift. Changes in supply of allochthonous 'drift-kelp' food are hypothesised as a trigger of change in urchin grazing behaviour, yet field tests are lacking. Here we conduct a suite of in situ behavioural surveys and manipulative experiments within kelp beds and on urchin barrens to examine foraging movements and evidence for a behavioural switch to an 'overgrazing mode' by the Australian urchin Heliocidaris erythrogramma (Echinometridae). Tracking urchins using time-lapse photography revealed urchin foraging to conform to a random walk model within kelp beds and on barrens. However, many individuals tended towards local movement within proximal crevices and movement was reduced in kelp beds compared to barrens. Directional movement of urchins toward newly available kelp was experimentally inducible, consistent with locally observed 'mobile-feeding-fronts' that develop at barrens-kelp interfaces. Habitat-specific feeding modes were also evidenced by herbivory assays which revealed urchin grazing rates to be high on both drift-kelp and standing kelp on barren grounds, while drift-kelp but not standing kelp was consumed at high rates within kelp beds. Time-lapse tracking of urchin foraging before/after addition of drift-kelp revealed a reduction in foraging across the reef surface after drift-kelp capture. Collectively, results indicate that the availability of drift-kelp is a pivotal trigger in determining urchin feeding modes which thus mediates the shift between alternative stable states for rocky reef ecosystems.

Ray Tracing Generalized Tube Primitives: Method and Applications

We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.

Reduced resistance to sediment-trapping turfs with decline of native kelp and establishment of an exotic kelp

Understanding the strength and type of interactions among species is vital to anticipate how ecosystems will respond to ongoing anthropogenic stressors. Here, we examine the ecological function of native (Ecklonia radiata) and invasive (Undaria pinnatifida) kelps in resisting shifts to sediment-trapping turf on reefs within the highly urbanized temperate Port Phillip Bay (PPB), Australia. Short-term (30 days) and long-term (232 days) manipulations demonstrated that kelp laminae can clear and maintain the substratum free of turfs, while conversely, removal of kelp leads to a proliferation of turfs. Analyses looking at the relationship between total length of E. radiata and U. pinnatifida and the area cleared of turf algae showed that the clearing effect of E. radiata over a year was greater than that of U. pinnatifida due to the annual die-back of the invasive. A natural experiment (608 days) identified that ongoing sea urchin (Heliocidaris erythrogramma) grazing led to native kelp bed decline, facilitating turf dominance. Even though U. pinnatifida establishes once native beds are disturbed, its ecological function in clearing turf is weaker than E. radiata, given its annual habit. In PPB, turfs represent the more persistent and problematic algal group and are likely changing the structure, function, and energy flows of shallow temperate reefs in this urbanised embayment.

CPU Isosurface Ray Tracing of Adaptive Mesh Refinement Data

Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. In this paper, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy-the octant method-which is continuous, adaptive and simple to implement. Furthermore, we present a generally applicable hybrid implicit isosurface ray-tracing method, which provides better rendering quality and performance than the built-in sampling-based approach in OSPRay. Finally, we integrate our octant method and hybrid isosurface geometry into OSPRay as a module, providing the ability to create high-quality interactive visualizations combining volume and isosurface representations of BS-AMR data. We evaluate the rendering performance, memory consumption and quality of our method on two gigascale block-structured AMR datasets.

A statistical framework for quantification and visualisation of positional uncertainty in deep brain stimulation electrodes

Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson's disease. Patient-specific computational modelling and visualisation have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode positions within the patient's head. The finite resolution of brain imaging, however, introduces uncertainty in electrode positions. The DBS stimulation settings for optimal patient response are sensitive to the relative positioning of DBS electrodes to a specific neural substrate (white/grey matter). In our contribution, we study positional uncertainty in the DBS electrodes for imaging with finite resolution. In a three-step approach, we first derive a closed-form mathematical model characterising the geometry of the DBS electrodes. Second, we devise a statistical framework for quantifying the uncertainty in the positional attributes of the DBS electrodes, namely the direction of longitudinal axis and the contact-centre positions at subvoxel levels. The statistical framework leverages the analytical model derived in step one and a Bayesian probabilistic model for uncertainty quantification. Finally, the uncertainty in contact-centre positions is interactively visualised through volume rendering and isosurfacing techniques. We demonstrate the efficacy of our contribution through experiments on synthetic and real datasets. We show that the spatial variations in true electrode positions are significant for finite resolution imaging, and interactive visualisation can be instrumental in exploring probabilistic positional variations in the DBS lead.

Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data

We present a framework for the analysis of uncertainty in isocontour extraction. The marching squares (MS) algorithm for isocontour reconstruction generates a linear topology that is consistent with hyperbolic curves of a piecewise bilinear interpolation. The saddle points of the bilinear interpolant cause topological ambiguity in isocontour extraction. The midpoint decider and the asymptotic decider are well-known mathematical techniques for resolving topological ambiguities. The latter technique investigates the data values at the cell saddle points for ambiguity resolution. The uncertainty in data, however, leads to uncertainty in underlying bilinear interpolation functions for the MS algorithm, and hence, their saddle points. In our work, we study the behavior of the asymptotic decider when data at grid vertices is uncertain. First, we derive closed-form distributions characterizing variations in the saddle point values for uncertain bilinear interpolants. The derivation assumes uniform and nonparametric noise models, and it exploits the concept of ratio distribution for analytic formulations. Next, the probabilistic asymptotic decider is devised for ambiguity resolution in uncertain data using distributions of the saddle point values derived in the first step. Finally, the confidence in probabilistic topological decisions is visualized using a colormapping technique. We demonstrate the higher accuracy and stability of the probabilistic asymptotic decider in uncertain data with regard to existing decision frameworks, such as deciders in the mean field and the probabilistic midpoint decider, through the isocontour visualization of synthetic and real datasets.

Photon Doppler velocimetry measurements of transverse surface velocities

The goal of this work was to develop a technique for making transverse surface velocity measures utilizing Photon Doppler Velocimetry (PDV). Such a task is achieved by transmitting light and collecting Doppler-shifted light at an angle relative to the normal axis, where measured velocities are representative of a component of the transverse velocity. Because surface characteristics have an intrinsic effect on light scatter, different surface preparations were explored to direct reflectivity, including diffusion by means of sandpapering, or increasing retroreflectivity by coating with microspheres, milling v-cuts, and electrochemically etching grooves. Testing of these surface preparations was performed using an experiment featuring a 30 mm diameter aluminum disk rotating at 6000 or 6600 RPM. A single PDV collimator was positioned along the rotational axis of the disk at various angles, resolving the apparent transverse velocity. To characterize surface preparations, light return and velocities were recorded as a function of probe angle ranging from 0° to 51° from the surface normal for each preparation. Polished and electrochemically etched surfaces did not provide enough reflected light to resolve a beat frequency; however, sandpapered surfaces, retroreflective microspheres, and milled v-cuts provided adequate reflected light for incidence angles up to 51°. Applications of the surface preparations were then studied in gas gun experiments. Retroreflective microspheres were studied in a planar impact experiment, and milled v-cuts were studied in an oblique impact experiment. A normal and transverse profile of particle velocity was resolved in the oblique impact experiment.

Interactive visual exploration and refinement of cluster assignments

Background

With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don’t properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data.

Results

In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes.

Conclusions

Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1813-7) contains supplementary material, which is available to authorized users.

Pathways for Theoretical Advances in Visualization

There is little doubt that having a theoretic foundation will benefit the field of visualization, including its main subfields. Because there has been a substantial amount of work on taxonomies and conceptual models in the visualization literature and some recent work on theoretic frameworks, such a theoretic foundation is not a foolish or impractical ambition. This article asks, "How can we build a theoretic foundation for visualization collectively as a community?" The authors envision the pathways for four different aspects of a theoretic foundation: taxonomies and ontologies, principles and guidelines, conceptual models and theoretic frameworks, and quantitative laws and theoretic systems.

View-Dependent Streamline Deformation and Exploration

Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.

Inverse Electrocardiographic Source Localization of Ischemia: An Optimization Framework and Finite Element Solution

With the goal of non-invasively localizing cardiac ischemic disease using body-surface potential recordings, we attempted to reconstruct the transmembrane potential (TMP) throughout the myocardium with the bidomain heart model. The task is an inverse source problem governed by partial differential equations (PDE). Our main contribution is solving the inverse problem within a PDE-constrained optimization framework that enables various physically-based constraints in both equality and inequality forms. We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice. Such a formulation was derived for the L₂-norm Tikhonov regularization and the total variation minimization. The subsequent numerical optimization was fulfilled by a primal-dual interior-point method tailored to our problem's specific structure. Our simulations used realistic, fiber-included heart models consisting of up to 18,000 nodes, much finer than any inverse models previously reported. With synthetic ischemia data we localized ischemic regions with roughly a 10% false-negative rate or a 20% false-positive rate under conditions up to 5% input noise. With ischemia data measured from animal experiments, we reconstructed TMPs with roughly 0.9 correlation with the ground truth. While precisely estimating the TMP in general cases remains an open problem, our study shows the feasibility of reconstructing TMP during the ST interval as a means of ischemia localization.

Uncertainty Visualization in Forward and Inverse Cardiac Models

Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.

Uncertainty Visualization in HARDI based on Ensembles of ODFs

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.

The relation of mild traumatic brain injury to chronic lapses of attention

We assessed the extent to which failures in sustained attention were associated with chronic mild traumatic brain injury (mTBI) deficits in cognitive control among college-age young adults with and without a history of sport-related concussion. Participants completed the ImPACT computer-based assessment and a modified flanker task. Results indicated that a history of mTBI, relative to healthy controls, was associated with inferior overall flanker task performance with a greater number of omission errors and more frequent sequentially occurring omission errors. Accordingly, these findings suggest that failures in the ability to maintain attentional vigilance may, in part, underlie mTBI-related cognition deficits.

White matter structure assessment from reduced HARDI data using low-rank polynomial approximations

Assessing white matter fiber orientations directly from DWI measurements in single-shell HARDI has many advantages. One of these advantages is the ability to model multiple fibers using fewer parameters than are required to describe an ODF and, thus, reduce the number of DW samples needed for the reconstruction. However, fitting a model directly to the data using Gaussian mixture, for instance, is known as an initialization-dependent unstable process. This paper presents a novel direct fitting technique for single-shell HARDI that enjoys the advantages of direct fitting without sacrificing the accuracy and stability even when the number of gradient directions is relatively low. This technique is based on a spherical deconvolution technique and decomposition of a homogeneous polynomial into a sum of powers of linear forms, known as a symmetric tensor decomposition. The fiber-ODF (fODF), which is described by a homogeneous polynomial, is approximated here by a discrete sum of even-order linear-forms that are directly related to rank-1 tensors and represent single-fibers. This polynomial approximation is convolved to a single-fiber response function, and the result is optimized against the DWI measurements to assess the fiber orientations and the volume fractions directly. This formulation is accompanied by a robust iterative alternating numerical scheme which is based on the Levenberg-Marquardt technique. Using simulated data and in vivo, human brain data we show that the proposed algorithm is stable, accurate and can model complex fiber structures using only 12 gradient directions.

Short communication: characterization of early postpartum estrous behavior and ovulation in lactating dairy cows using radiotelemetry

The objective of this study was to describe early postpartum estrous behavior and ovulation in lactating dairy cows using radiotelemetry. Cows (n=50) were continuously monitored for behavioral estrus with a radiotelemetric system, HeatWatch II (CowChips LLC, Manalapan, NJ), from d 14 to approximately d 49 postpartum. Blood collection for analysis of progesterone and ovarian ultrasonography were performed once weekly starting on d 14. First ovulation was associated with behavioral estrus in 5 cows and occurred at 28.2±10.8 d (mean±SD; range 17 to 40 d). The average duration of estrus was 6.0±4.9 h (range 3 to 12.2 h), and the mean number of standing events was 18.4±8.9 (range 4 to 26). Based on progesterone concentrations of ≥1 ng/mL, estimated first postpartum ovulation occurred at 25.1±10.4 d (range 10 to 49 d) for 38 animals without evidence of behavioral estrus. The interval to estimated first ovulation without behavioral estrus was not different from the interval to first ovulation associated with behavioral estrus. Level of milk production and body condition score loss did not affect the interval to estimated first ovulation without estrus or first ovulation associated with estrus. Six animals did not show evidence of ovulation based on progesterone concentration, whereas 1 cow showed evidence of estrous behavior on the day before removal from the study. The majority of first postpartum ovulations (38/43; 88.4%) were not associated with behavioral estrus.

An optimization framework for inversely estimating myocardial transmembrane potentials and localizing ischemia

By combining a static bidomain heart model with a torso conduction model, we studied the inverse electrocardiographic problem of computing the transmembrane potentials (TMPs) throughout the myocardium from a body-surface potential map, and then used the recovered potentials to localize myocardial ischemia. Our main contribution is solving the inverse problem within a constrained optimization framework, which is a generalization of previous methods for calculating transmembrane potentials. The framework offers ample flexibility for users to apply various physiologically-based constraints, and is well supported by mature algorithms and solvers developed by the optimization community. By avoiding the traditional inverse ECG approach of building the lead-field matrix, the framework greatly reduces computation cost and, by setting the associated forward problem as a constraint, the framework enables one to flexibly set individualized resolutions for each physical variable, a desirable feature for balancing model accuracy, ill-conditioning and computation tractability. Although the task of computing myocardial TMPs at an arbitrary time instance remains an open problem, we showed that it is possible to obtain TMPs with moderate accuracy during the ST segment by assuming all cardiac cells are at the plateau phase. Moreover, the calculated TMPs yielded a good estimate of ischemic regions, which was of more clinical interest than the voltage values themselves. We conducted finite element simulations of a phantom experiment over a 2D torso model with synthetic ischemic data. Preliminary results indicated that our approach is feasible and suitably accurate for the common case of transmural myocardial ischemia.

Marine reserves reduce risk of climate-driven phase shift by reinstating size- and habitat-specific trophic interactions

Spatial closures in the marine environment are widely accepted as effective conservation and fisheries management tools. Given increasing human-derived stressors acting on marine ecosystems, the need for such effective action is urgently clear. Here we explore mechanisms underlying the utility of marine reserves to reinstate trophic dynamics and to increase resilience of kelp beds against climate-driven phase shift to sea urchin barrens on the rapidly warming Tasmanian east coast. Tethering and tagging experiments were used to examine size- and shelter-specific survival of the range-extending sea urchin Centrostephanus rodgersii (Diadematidae) translocated to reefs inside and outside no-take Tasmanian marine reserves. Results show that survival rates of C. rodgersii exposed on flat reef substratum by tethering were approximately seven times (small urchins 10.1 times; large urchins 6.1 times) lower on protected reef within marine reserve boundaries (high abundance of large predatory-capable lobsters) compared to fished reef (large predatory lobsters absent). When able to seek crevice shelter, tag-resighting models estimated that mortality rates of C. rodgersii were lower overall but remained 3.3 times (small urchins 2.1 times; large urchins 6.4 times) higher in the presence of large lobsters inside marine reserves, with higher survival of small urchins owing to greater access to crevices relative to large urchins. Indeed, shelter was 6.3 times and 3.1 times more important to survival of small and large urchins, respectively, on reserved relative to fished reef. Experimental results corroborate with surveys throughout the range extension region, showing greater occurrence of overgrazing on high-relief rocky habitats where shelter for C. rodgersii is readily available. This shows that ecosystem impacts mediated by range extension of such habitat-modifying organisms will be heterogeneous in space, and that marine systems with a more natural complement of large and thus functional predators, as achievable within no-take reserves, will minimize local risk of phase shifts by reinstating size and habitat-specific predator-prey dynamics eroded by fishing. Importantly, our findings also highlight the crucial need to account for the influence of size dynamics and habitat complexity on rates of key predator-prey interactions when managing expectations of ecosystem-level responses within marine reserve boundaries.

Biomedical Visual Computing: Case Studies and Challenges

Advances in computational geometric modeling, imaging, and simulation let researchers build and test models of increasing complexity, generating unprecedented amounts of data. As recent research in biomedical applications illustrates, visualization will be critical in making this vast amount of data usable; it's also fundamental to understanding models of complex phenomena.

INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS

The probability density function (PDF), and its corresponding cumulative density function (CDF), provide direct statistical insight into the characterization of a random process or field. Typically displayed as a histogram, one can infer probabilities of the occurrence of particular events. When examining a field over some two-dimensional domain in which at each point a PDF of the function values is available, it is challenging to assess the global (stochastic) features present within the field. In this paper, we present a visualization system that allows the user to examine two-dimensional data sets in which PDF (or CDF) information is available at any position within the domain. The tool provides a contour display showing the normed difference between the PDFs and an ansatz PDF selected by the user and, furthermore, allows the user to interactively examine the PDF at any particular position. Canonical examples of the tool are provided to help guide the reader into the mapping of stochastic information to visual cues along with a description of the use of the tool for examining data generated from an uncertainty quantification exercise accomplished within the field of electrophysiology.

Microfluidic laminate-based phantom for diffusion tensor-magnetic resonance imaging (DT-MRI)

This paper reports fabrication of a magnetic resonance imaging (MRI) phantom created by stacking of multiple thin polydimethylsiloxane (PDMS) layers. PDMS is spin coated on SU-8 molds to obtain the desired layer thickness and imprints of the microchannel patterns that define the phantom geometry. This paper also identifies the unique challenges related to the fabrication and assembly of multiple thin layers and reports for the first time assembly of a large number of thin laminates of this nature. Use of photolithography techniques allows us to create a wide range of phantom geometries. The target dimensions of the phantoms reported here are (i) a stack of 30 thin PDMS layers of 10 µm thickness (ii) curved 5 µm × 5 µm microchannels with 8.7 µm spacing, and (iii) straight 5 µm × 5 µm microchannels with 3.6 µm spacing. SEM scans of the assembled phantoms show open microchannels and a monolithic cross-section with no visible interface between PDMS layers. Based on the results of diffusion tensor magnetic resonance imaging (DT-MRI) scan, the anisotropic diffusion of water molecules due to the physical restriction of the microchannels was detected, which means that the phantom can be used to calibrate and optimize MRI instrumentation.

Finite-element-based discretization and regularization strategies for 3-D inverse electrocardiography

We consider the inverse electrocardiographic problem of computing epicardial potentials from a body-surface potential map. We study how to improve numerical approximation of the inverse problem when the finite-element method is used. Being ill-posed, the inverse problem requires different discretization strategies from its corresponding forward problem. We propose refinement guidelines that specifically address the ill-posedness of the problem. The resulting guidelines necessitate the use of hybrid finite elements composed of tetrahedra and prism elements. Also, in order to maintain consistent numerical quality when the inverse problem is discretized into different scales, we propose a new family of regularizers using the variational principle underlying finite-element methods. These variational-formed regularizers serve as an alternative to the traditional Tikhonov regularizers, but preserves the L(2) norm and thereby achieves consistent regularization in multiscale simulations. The variational formulation also enables a simple construction of the discrete gradient operator over irregular meshes, which is difficult to define in traditional discretization schemes. We validated our hybrid element technique and the variational regularizers by simulations on a realistic 3-D torso/heart model with empirical heart data. Results show that discretization based on our proposed strategies mitigates the ill-conditioning and improves the inverse solution, and that the variational formulation may benefit a broader range of potential-based bioelectric problems.

Detection of crossing white matter fibers with high-order tensors and rank-k decompositions

Fundamental to high angular resolution diffusion imaging (HARDI), is the estimation of a positive-semidefinite orientation distribution function (ODF) and extracting the diffusion properties (e.g., fiber directions). In this work we show that these two goals can be achieved efficiently by using homogeneous polynomials to represent the ODF in the spherical deconvolution approach, as was proposed in the Cartesian Tensor-ODF (CT-ODF) formulation. Based on this formulation we first suggest an estimation method for positive-semidefinite ODF by solving a linear programming problem that does not require special parameterization of the ODF. We also propose a rank-k tensor decomposition, known as CP decomposition, to extract the fibers information from the estimated ODF. We show that this decomposition is superior to the fiber direction estimation via ODF maxima detection as it enables one to reach the full fiber separation resolution of the estimation technique. We assess the accuracy of this new framework by applying it to synthetic and experimentally obtained HARDI data.

Monitoring associations between clade-level variation, overall community structure and ecosystem function in enhanced biological phosphorus removal (EBPR) systems using terminal-restriction fragment length polymorphism (T-RFLP)

The role of Candidatus "Accumulibacter phosphatis" (Accumulibacter) in enhanced biological phosphorus removal (EBPR) is well established but the relevance of different Accumulibacter clades to the performance of EBPR systems is unknown. We developed a terminal-restriction fragment length polymorphism (T-RFLP) technique to monitor changes in the relative abundance of key members of the bacterial community, including Accumulibacter clades, in four replicate mini-sequencing batch reactors (mSBRs) operated for EBPR over a 35-day period. The ability of the T-RFLP technique to detect trends was confirmed using fluorescence in situ hybridisation (FISH). EBPR performance varied between reactors and over time; by day 35, performance was maintained in mSBR2 whilst it had deteriorated in mSBR1. However, reproducible trends in structure-function relationships were detected in the mSBRs. EBPR performance was strongly associated with the relative abundance of total Accumulibacter. A shift in the ratio of the dominant Accumulibacter clades was also detected, with Type IA associated with good EBPR performance and Type IIC associated with poor EBPR performance. Changes in ecosystem function of the mSBRs in the early stages of the experiment were more closely associated with changes in the abundance of (unknown) members of the flanking community than of either Accumulibacter or Candidatus "Competibacter phosphatis". This study therefore reveals a hitherto unrecorded and complex relationship between Accumulibacter clades, the flanking community and ecosystem function of laboratory-scale EBPR systems.

POCS-enhanced correction of motion artifacts in parallel MRI

A new method for correction of MRI motion artifacts induced by corrupted k-space data, acquired by multiple receiver coils such as phased arrays, is presented. In our approach, a projections onto convex sets (POCS)-based method for reconstruction of sensitivity encoded MRI data (POCSENSE) is employed to identify corrupted k-space samples. After the erroneous data are discarded from the dataset, the artifact-free images are restored from the remaining data using coil sensitivity profiles. The error detection and data restoration are based on informational redundancy of phased-array data and may be applied to full and reduced datasets. An important advantage of the new POCS-based method is that, in addition to multicoil data redundancy, it can use a priori known properties about the imaged object for improved MR image artifact correction. The use of such information was shown to improve significantly k-space error detection and image artifact correction. The method was validated on data corrupted by simulated and real motion such as head motion and pulsatile flow.

Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift

A key consideration in assessing impacts of climate change is the possibility of synergistic effects with other human-induced stressors. In the ocean realm, climate change and overfishing pose two of the greatest challenges to the structure and functioning of marine ecosystems. In eastern Tasmania, temperate coastal waters are warming at approximately four times the global ocean warming average, representing the fastest rate of warming in the Southern Hemisphere. This has driven range extension of the ecologically important long-spined sea urchin (Centrostephanus rodgersii), which has now commenced catastrophic overgrazing of productive Tasmanian kelp beds leading to loss of biodiversity and important rocky reef ecosystem services. Coincident with the overgrazing is heavy fishing of reef-based predators including the spiny lobster Jasus edwardsii. By conducting experiments inside and outside Marine Protected Areas we show that fishing, by removing large predatory lobsters, has reduced the resilience of kelp beds against the climate-driven threat of the sea urchin and thus increased risk of catastrophic shift to widespread sea urchin barrens. This shows that interactions between multiple human-induced stressors can exacerbate nonlinear responses of ecosystems to climate change and limit the adaptive capacity of these systems. Management actions focused on reducing the risk of catastrophic phase shift in ecosystems are particularly urgent in the face of ongoing warming and unprecedented levels of predator removal from the world's oceans.

Resolution strategies for the finite-element-based solution of the ECG inverse problem

Successful employment of numerical techniques for the solution of forward and inverse ECG problems requires the ability to both quantify and minimize approximation errors introduced as part of the discretization process. Our objective is to develop discretization and refinement strategies involving hybrid-shaped finite elements so as to minimize approximation errors for the ECG inverse problem. We examine both the ill-posedness of the mathematical inverse problem and the ill-conditioning of the discretized system in order to propose strategies specifically designed for the ECG inverse problem. We demonstrate that previous discretization and approximation strategies may worsen the properties of the inverse problem approximation. We then demonstrate the efficacy of our strategies on both a simplified and a realistic 2-D torso model.

Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples

Many simulation studies in biomedicine are based on a similar sequence of processing steps, starting from images and running through geometric model generation, assignment of tissue properties, numerical simulation and visualization of the results--a process known as image-based geometric modelling and simulation. We present an overview of software systems for implementing such a sequence both within highly integrated problem-solving environments and in the form of loosely integrated pipelines. Loose integration in this case indicates that individual programs function largely independently but communicate through files of a common format and support simple scripting, so as to automate multiple executions wherever possible. We then describe three specific applications of such pipelines to translational biomedical research in electrophysiology.

A molecular approach to identify prey of the southern rock lobster

We demonstrate the use of molecular techniques to detect specific prey consumed by the southern rock lobster (Jasus edwardsii). A quick and non-lethal method was used to collect rock lobster faecal material and a molecular protocol was employed to isolate prey DNA from faecal samples. The isolated DNA was amplified using the polymerase chain reaction (PCR) with PCR primers designed to target specific prey items. Feeding experiments determined that DNA from black-lipped abalone (Haliotis rubra) and sea urchins (Centrostephanus rodgersii and Heliocidaris erythrogramma) can be detected in rock lobster faecal samples within seven hours and remains present for up to 60 h after ingestion.

A randomised controlled trial of the reciprocating syringe in arthrocentesis

OBJECTIVE

To evaluate the outcomes of arthrocentesis with the new highly controllable, one handed reciprocating procedure syringe compared with a conventional syringe.

METHODS

100 arthrocentesis procedures were randomised between the reciprocating syringe and the conventional syringe. Outcome measures included patient pain, procedure duration, operator satisfaction, synovial fluid volume, cell counts, and complications.

RESULTS

50 arthrocentesis procedures with the conventional syringe resulted in a mean (SD) procedure time of 3.39 (1.88) minutes, a mean VAPS (patient pain) score of 5.35 (3.15), and a mean VASS (operator satisfaction) score of 4.88 (1.92); 30 of the 50 subjects experienced moderate to severe pain (VAPS score 5 or greater) during arthrocentesis. In contrast, the reciprocating syringe resulted in a reduced procedure time of 1.94 (1.14) minutes (p<0.001), a reduced VAPS (patient pain) score of 2.54 (1.60) (p<0.001), and an increased VASS (operator satisfaction) score of 8.91 (0.79) (p<0.001). Only five of the 50 of subjects experienced moderate to severe pain with the reciprocating syringe. Synovial cell counts were similar between the two syringes (p>0.05), but there was a trend toward greater volume (greater synovial fluid yield) and fewer red blood cells with the reciprocating syringe.

CONCLUSIONS

Arthrocentesis with a conventional syringe results in moderate to severe pain in 60% of subjects. The reciprocating syringe prevents significant pain, reduces procedure time, and improves physician performance of arthrocentesis. The reciprocating syringe is superior to the conventional syringe in arthrocentesis.

Caspase-9 regulation: an update

The complex sequence of events leading to apoptotic cell death is governed by an elaborate regulatory scheme involving the actions of both initiator and executioner proteases. Among the most intensively studied of the initiator caspases is caspase-9, an essential throughput element in the so-called intrinsic or mitochondrially gated pathway of apoptosis. Previous reviews have described the proteolytic processing and activation of this protease in much detail; here we provide an update on caspase-9 regulation. A comprehensive description of the intra- and intermolecular events involved in modulating protein expression and activity are presented. Particular emphasis is placed on the role alternative splicing plays in the expression of functionally divergent protein isoforms, as well as, the participation of specific post-translational events in regulating caspase-9 activity. Such discrete modulation in reported activity characterizes, not only the pivotal role of this protease in the final commitment process itself, but also emphasizes the more general interplay that exists between mutually opposing cytotoxic and cytoprotective influences in maintaining cellular homeostasis.

Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels

Anisotropic diffusion filtering is widely used for MR image enhancement. However, the anisotropic filter is nonoptimal for MR images with spatially varying noise levels, such as images reconstructed from sensitivity-encoded data and intensity inhomogeneity-corrected images. In this work, a new method for filtering MR images with spatially varying noise levels is presented. In the new method, a priori information regarding the image noise level spatial distribution is utilized for the local adjustment of the anisotropic diffusion filter. Our new method was validated and compared with the standard filter on simulated and real MRI data. The noise-adaptive method was demonstrated to outperform the standard anisotropic diffusion filter in both image error reduction and image signal-to-noise ratio (SNR) improvement. The method was also applied to inhomogeneity-corrected and sensitivity encoding (SENSE) images. The new filter was shown to improve segmentation of MR brain images with spatially varying noise levels.

POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging

A novel method for iterative reconstruction of images from undersampled MRI data acquired by multiple receiver coil systems is presented. Based on Projection onto Convex Sets (POCS) formalism, the method for SENSitivity Encoded data reconstruction (POCSENSE) can be readily modified to include various linear and nonlinear reconstruction constraints. Such constraints may be beneficial for reconstructing highly and overcritically undersampled data sets to improve image quality. POCSENSE is conceptually simple and numerically efficient and can reconstruct images from data sampled on arbitrary k-space trajectories. The applicability of POCSENSE for image reconstruction with nonlinear constraining was demonstrated using a wide range of simulated and real MRI data.