In recent years there has been significant growth in the use of patient-specific models to predict the effects of deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge. In this paper, we deploy the interactive visualization system ImageVis3D Mobile in an evaluation environment to visualize models of Parkinson’s disease patients who received DBS therapy. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independently of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system can be used to guide clinical decision making for selection of DBS settings.

Keywords: scidac, dbs

As neuromodulation therapy has grown, so has the recognition that computational models can provide important insights into the design, operation, and clinical application of neurostimulation systems. Models of deep brain stimulation and spinal cord stimulation have advanced over recent decades from simple, stereotyped models to sophisticated patient-specific models that can incorporate many important details of the stimulation system and the attributes of individual subjects. Models have been used to make detailed predictions of the bioelectric fields produced during stimulation. These predictions have been used as a starting point for further analyses such as stimulation safety, neural response, neurostimulation system design, or clinical outcomes. This chapter provides a review of recent advances and anticipated future directions in computational modeling of neuromodulation.

Background: Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterization of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories.

Methods: A cross-sectional study of 2-6 year old children with autism was conducted using diffusion tensor imaging combined with a novel statistical approach employing fractional anisotropy distributions. 58 children aged 18-79 months were imaged: 33 were diagnosed with autism, 8 with general developmental delay (DD), and 17 were typically developing (TD). Fractional anisotropy values within global white matter, cortical lobes, and the cerebellum were measured and transformed to random F distributions for each subject. Each distribution of values for a region was summarized by estimating delta, the estimated mean and standard deviation of the approximating F for each distribution.

Results: The estimated delta parameter, delta-hat, was significantly decreased in individuals with autism compared to the combined control group. This was true in all cortical lobes, as well as in the cerebellum, but differences were strongest in the temporal lobe. Predicted developmental trajectories of delta-hat across the age range in the sample showed patterns that partially distinguished the groups. Exploratory analyses suggested that the variability, rather than the central tendency, component of delta-hat was the driving force behind these results. Conclusions: White matter in young children with autism appears to be abnormally homogeneous, which may reflect poorly organized or differentiated pathways, particularly in the temporal lobe, which is important for social and emotional cognition.

Objective
During neovascularization, the end result is a new functional microcirculation composed of a network of mature microvessels with specific topologies. Although much is known concerning the mechanisms underlying the initiation of angiogenesis, it remains unclear how the final architecture of microcirculatory beds is regulated. To begin to address this, we determined the impact of angiogenic neovessel prepatterning on the final microvascular network topology using a model of implant neovascularization.

Methods and Results
We used 3D direct-write bioprinting or physical constraints in a manner permitting postangiogenesis vascular remodeling and adaptation to pattern angiogenic microvascular precursors (neovessels formed from isolated microvessel segments) in 3D collagen gels before implantation and subsequent network formation. Neovasculatures prepatterned into parallel arrays formed functional networks after 4 weeks postimplantation but lost the prepatterned architecture. However, maintenance of uniaxial physical constraints during postangiogenesis remodeling of the implanted neovasculatures produced networks with aligned microvessels, as well as an altered proportional distribution of arterioles, capillaries, and venules.

Conclusion
Here we show that network topology resulting from implanted microvessel precursors is independent from prepatterning of precursors but can be influenced by a patterning stimulus involving tissue deformation during postangiogenesis remodeling and maturation.

Liquid–liquid extraction is a typical multi-fluid problem in chemical engineering where two types of immiscible fluids are mixed together. Mixing of two-phase fluids results in a time-varying fluid density distribution, quantitatively indicating the presence of liquid phases. For engineers who design extraction devices, it is crucial to understand the density distribution of each fluid, particularly flow regions that have a high concentration of the dispersed phase. The propagation of regions of high density can be studied by examining the topology of isosurfaces of the density data. We present a topology-based approach to track the splitting and merging events of these regions using the Reeb graphs. Time is used as the third dimension in addition to two-dimensional (2D) point-based simulation data. Due to low time resolution of the input data set, a physics-based interpolation scheme is required in order to improve the accuracy of the proposed topology tracking method. The model used for interpolation produces a smooth time-dependent density field by applying Lagrangian-based advection to the given simulated point cloud data, conforming to the physical laws of flow evolution. Using the Reeb graph, the spatial and temporal locations of bifurcation and merging events can be readily identified supporting in-depth analysis of the extraction process.

Malfatti's problem, first published in 1803, is commonly understood to ask fitting three circles into a given triangle such that they are tangent to each other, externally, and such that each circle is tangent to a pair of the triangle's sides. There are many solutions based on geometric constructions, as well as generalizations in which the triangle sides are assumed to be circle arcs. A generalization that asks to fit six circles into the triangle, tangent to each other and to the triangle sides, has been considered a good example of a problem that requires sophisticated numerical iteration to solve by computer. We analyze this problem and show how to solve it quickly.

Keywords: Malfatti's problem, circle packing, geometric constraint solving, GPU programming

We demonstrate the application of topological analysis techniques to the rather unexpected domain of software visualization. We collect a memory reference trace from a running program, recasting the linear flow of trace records as a high-dimensional point cloud in a metric space. We use topological persistence to automatically detect significant circular structures in the point cloud, which represent recurrent or cyclical runtime program behaviors. We visualize such recurrences using radial plots to display their time evolution, offering multi-scale visual insights, and detecting potential candidates for memory performance optimization. We then present several case studies to demonstrate some key insights obtained using our techniques.

Keywords: scidac

The current work presents a computational pipeline to simulate transcranial direct current stimulation from image based models of the head with SCIRun [15]. The pipeline contains all the steps necessary to carry out the simulations and is supported by a complete suite of open source software tools: image visualization, segmentation, mesh generation, tDCS electrode generation and efficient tDCS forward simulation.

In this paper, we propose a new method for longitudinal shape analysis that ts a linear mixed-eects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a xed eect and individual trends as random eects. The statistical signi cance of the estimated trends are evaluated using speci cally designed permutation tests. We also develop a permutation test based on the Hotelling T² statistic to compare the average shapes trends between two populations. We demonstrate the bene ts of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

Keywords: Computer Science

There are many applications in MRI where it is desirable to have high spatial and high temporal resolution. This can be achieved by undersampling of k-space and requires special techniques for reconstruction. Even if undersampling artifacts are removed, sharpness of the edges can be a problem. We propose a new technique that uses the gradient from a reference image to improve the quality of the edges in the reconstructed image along with a spatio-temporal constraint to reduce aliasing artifacts and noise. The reference is created from undersampled dynamic data by combining several adjacent frames. The method was tested on undersampled radial DCE MRI data with little respiratory motion. The proposed method was compared to reconstruction using the spatio-temporal constrained reconstruction. Sharper edges and an increase in the contrast was observed by using the proposed method.

A GPU implementation is given for real-time visualization of aggregate eye movements (gaze) via heatmaps. Parallelization of the algorithm leads to substantial speedup over its CPU-based implementation and, for the first time, allows real-time rendering of heatmaps atop video. GLSL shader colorization allows the choice of color ramps. Several luminance-based color maps are advocated as alternatives to the popular rainbow color map, considered inappropriate (harmful) for depiction of (relative) gaze distributions.

Statistical analysis of shapes, performed by constructing an atlas composed of an average model of shapes within a population and associated deformation maps, is a fundamental aspect of medical imaging studies. Usual methods for constructing a shape atlas require point correspondences across subjects, which are difficult in practice. By contrast, methods based on currents do not require correspondence. However, existing atlas construction methods using currents suffer from two limitations. First, the template current is not in the form of a topologically correct mesh, which makes direct analysis on shapes difficult. Second, the deformations are parametrized by vectors at the same location as the normals of the template current which often provides a parametrization that is more dense than required. In this paper, we propose a novel method for constructing shape atlases using currents where topology of the template is preserved and deformation parameters are optimized independently of the shape parameters. We use an L¹-type prior that enables us to adaptively compute sparse and low dimensional parameterization of deformations.We show an application of our method for comparing anatomical shapes of patients with Down’s syndrome and healthy controls, where the sparse parametrization of diffeomorphisms decreases the parameter dimension by one order of magnitude.

This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L¹ prior to the objective function, which is optimized using the FISTA algorithm.

Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.

Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our method on a synthetic shape database and clinical data from a study that seeks to quantify growth differences in subjects at risk for autism.

Few large-scale studies have been done to characterize the normal human brain white matter growth in the first years of life. We investigated white matter maturation patterns in major fiber pathways in a large cohort of healthy young children from birth to age two using diffusion parameters fractional anisotropy (FA), radial diffusivity (RD) and axial diffusivity (RD). Ten fiber pathways, including commissural, association and projection tracts, were examined with tract-based analysis, providing more detailed and continuous spatial developmental patterns compared to conventional ROI based methods. All DTI data sets were transformed to a population specific atlas with a group-wise longitudinal large deformation diffeomorphic registration approach. Diffusion measurements were analyzed along the major fiber tracts obtained in the atlas space. All fiber bundles show increasing FA values and decreasing radial and axial diffusivities during development in the first 2 years of life. The changing rates of the diffusion indices are faster in the first year than the second year for all tracts. RD and FA show larger percentage changes in the first and second years than AD. The gender effects on the diffusion measures are small. Along different spatial locations of fiber tracts, maturation does not always follow the same speed. Temporal and spatial diffusion changes near cortical regions are in general smaller than changes in central regions. Overall developmental patterns revealed in our study confirm the general rules of white matter maturation. This work shows a promising framework to study and analyze white matter maturation in a tract-based fashion. Compared to most previous studies that are ROI-based, our approach has the potential to discover localized development patterns associated with fiber tracts of interest.

In many areas, scientists deal with increasingly high-dimensional data sets. An important aspect for these scientists is to gain a qualitative understanding of the process or system from which the data is gathered. Often, both input variables and an outcome are observed and the data can be characterized as a sample from a high-dimensional scalar function. This work presents the R package msr for exploratory data analysis of multivariate scalar functions based on the Morse-Smale complex. The Morse-Smale complex provides a topologically meaningful decomposition of the domain. The msr package implements a discrete approximation of the Morse-Smale complex for data sets. In previous work this approximation has been exploited for visualization and partition-based regression, which are both supported in the msr package. The visualization combines the Morse-Smale complex with dimension-reduction techniques for a visual summary representation that serves as a guide for interactive exploration of the high-dimensional function. In a similar fashion, the regression employs a combination of linear models based on the Morse-Smale decomposition of the domain. This regression approach yields topologically accurate estimates and facilitates interpretation of general trends and statistical comparisons between partitions. In this manner, the msr package supports high-dimensional data understanding and exploration through the Morse-Smale complex.

Advances in surveillance science have supported public health agencies in tracking and responding to disease outbreaks. Increasingly, epidemiologists have been tasked with interpreting multiple streams of heterogeneous data arising from varied surveillance systems. As a result public health personnel have experienced an overload of plots and charts as information visualization techniques have not kept pace with the rapid expansion in data availability. This study sought to advance the science of public health surveillance data visualization by conceptualizing a visual paradigm that provides an 'epidemiological canvas' for detection, monitoring, exploration and discovery of regional infectious disease activity and developing a software prototype of an 'infectious disease weather map'. Design objectives were elucidated and the conceptual model was developed using cognitive task analysis with public health epidemiologists. The software prototype was pilot tested using retrospective data from a large, regional pediatric hospital, and gastrointestinal and respiratory disease outbreaks were re-created as a proof of concept.