Purpose: Clinical evaluation of a \"virtual\" methodology for providing 6 degrees of freedom (6DOF) patient set-up corrections and comparison to corrections facilitated by a 6DOF robotic couch.

Methods: A total of 55 weekly in-room image-guidance computed tomographic (CT) scans were acquired using a CT-on-rails for 11 pelvic and head and neck cancer patients treated at our facility. Fusion of the CT-of-the-day to the simulation CT allowed prototype virtual 6DOF correction software to calculate the translations, single couch yaw, and beam-specific gantry and collimator rotations necessary to effectively reproduce the same corrections as a 6DOF robotic couch. These corrections were then used to modify the original treatment plan beam geometry and this modified plan geometry was applied to the CT-of-the-day to evaluate the dosimetric effects of the virtual correction method. This virtual correction dosimetry was compared with calculated geometric and dosimetric results for an explicit 6DOF robotic couch correction methodology.

Results: A (2\%, 2mm) gamma analysis comparing dose distributions created using the virtual corrections to those from explicit corrections showed that an average of 95.1\% of all points had a gamma of 1 or less, with a standard deviation of 3.4\%. For a total of 470 dosimetric metrics (ie, maximum and mean dose statistics for all relevant structures) compared for all 55 image-guidance sessions, the average dose difference for these metrics between the plans employing the virtual corrections and the explicit corrections was -0.12\% with a standard deviation of 0.82\%; 97.9\% of all metrics were within 2\%.

Conclusions: Results showed that the virtual corrections yielded dosimetric distributions that were essentially equivalent to those obtained when 6DOF robotic corrections were used, and that always outperformed the most commonly employed clinical approach of 3 translations only. This suggests

In this paper, a method for interactive direct volume rendering is proposed that computes ambient occlusion effects for visualizations that combine both volumetric and geometric primitives, specifically tube shaped geometric objects representing streamlines, magnetic field lines or DTI fiber tracts. The proposed algorithm extends the recently proposed Directional Occlusion Shading model to allow the rendering of those geometric shapes in combination with a context providing 3D volume, considering mutual occlusion between structures represented by a volume or geometry.

Keywords: scidac, vacet, kaust, nvidia

The Uintah Software framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids together with a novel asynchronous task-based approach with fully automated load balancing. As Uintah is often used to solve compressible, low-Mach combustion applications, it is important to have a scalable linear solver. While there are many such solvers available, the scalability of those codes varies greatly. The hypre software offers a range of solvers and pre-conditioners for different types of grids. The weak scalability of Uintah and hypre is addressed for particular examples when applied to an incompressible flow problem relevant to combustion applications. After careful software engineering to reduce start-up costs, much better than expected weak scalability is seen for up to 100K cores on NSFs Kraken architecture and up to 200K+ cores, on DOEs new Titan machine.

Keywords: uintah, csafe

Design studies are an increasingly popular form of problem-driven visualization research, yet there is little guidance available about how to do them effectively. In this paper we reflect on our combined experience of conducting twenty-one design studies, as well as reading and reviewing many more, and on an extensive literature review of other field work methods and methodologies. Based on this foundation we provide definitions, propose a methodological framework, and provide practical guidance for conducting design studies. We define a design study as a project in which visualization researchers analyze a specific real-world problem faced by domain experts, design a visualization system that supports solving this problem, validate the design, and reflect about lessons learned in order to refine visualization design guidelines. We characterize two axes—a task clarity axis from fuzzy to crisp and an information location axis from the domain expert’s head to the computer—and use these axes to reason about design study contributions, their suitability, and uniqueness from other approaches. The proposed methodological framework consists of 9 stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. For each stage we provide practical guidance and outline potential pitfalls. We also conducted an extensive literature survey of related methodological approaches that involve a significant amount of qualitative field work, and compare design study methodology to that of ethnography, grounded theory, and action research.

We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline functions, capturing growth with only a few descriptive parameters. An unbiased template baseline function is also jointly estimated. Solution is demonstrated via energy minimization with an extension to simultaneous modeling of trajectories for multiple subjects. The new framework is validated with synthetic data and applied to longitudinal DTI from 15 infants. Interpretation of estimated model growth parameters is facilitated by visualization in the original coordinate space of fiber tracts.

With the advent of advanced imaging techniques, genotyping, and methods to assess clinical and biological progression, there is a growing need for a unified framework that could exploit information available from multiple sources to aid diagnosis and the identification of early signs of Alzheimer’s disease (AD). We propose a modeling strategy using supervised feature extraction to optimally combine highdimensional imaging modalities with several other low-dimensional disease risk factors. The motivation is to discover new imaging biomarkers and use them in conjunction with other known biomarkers for prognosis of individuals at high risk of developing AD. Our framework also has the ability to assess the relative importance of imaging modalities for predicting AD conversion. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD, only using information available at baseline.

Keywords: adni

Many centers are now using high-density microelectrodes during traditional intracranial electroencephalography (iEEG) both for research and clinical purposes. These microelectrodes are FDA-approved and integrate into clinical EEG acquisition systems. However, the electrical characteristics of these electrodes are poorly described and clinical systems were not designed to use them; thus, it is possible that this shift into clinical practice could have unintended consequences. In this study, we characterized the impedance of over 100 commercial macro- and microelectrodes using electrochemical impedance spectroscopy (EIS) to determine how electrode properties could affect signal acquisition and interpretation. The EIS data were combined with the published specifications of several commercial EEG systems to design digital filters that mimic the behavior of the electrodes and amplifiers. These filters were used to analyze simulated brain signals that contain a mixture of characteristic features commonly observed in iEEG. Each output was then processed with several common quantitative EEG measurements. Our results show that traditional macroelectrodes had low impedances and produced negligible distortion of the original signal. Brain tissue and electrical wiring also had negligible filtering effects. However, microelectrode impedances were much higher and more variable than the macroelectrodes. When connected to clinical amplifiers, higher impedance electrodes produced considerable distortion of the signal at low frequencies (

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

A fundamental step in stitching several pictures to form a larger mosaic is the computation of boundary seams that minimize the visual artifacts in the transition between images. Current seam computation algorithms use optimization methods that may be slow, sequential, memory intensive, and prone to finding suboptimal solutions related to local minima of the chosen energy function. Moreover, even when these techniques perform well, their solution may not be perceptually ideal (or even good). Such an inflexible approach does not allow the possibility of user-based improvement. This paper introduces the Panorama Weaving technique for seam creation and editing in an image mosaic. First, Panorama Weaving provides a procedure to create boundaries for panoramas that is fast, has low memory requirements and is easy to parallelize. This technique often produces seams with lower energy than the competing global technique. Second, it provides the first interactive technique for the exploration of the seam solution space. This powerful editing capability allows the user to automatically extract energy minimizing seams given a sparse set of constraints. With a variety of empirical results, we show how Panorama Weaving allows the computation and editing of a wide range of digital panoramas including unstructured configurations.

Keywords: digital panoramas, interactive image boundaries, panorama editing, panorama seams

Purpose: This project proposes using a real tissue phantom for 4D tissue deformation reconstruction (4DTDR) and 4D deformable image registration (DIR) validation, which allows for the complete verification of the motion path rather than limited end-point to end-point of motion.

Methods: Three electro-magnetic-tracking (EMT) fiducials were implanted into fresh porcine liver that was subsequently animated in a clinically realistic phantom. The animation was previously shown to be similar to organ motion, including hysteresis, when driven using a real patient's breathing pattern. For this experiment, 4DCTs and EMT traces were acquired when the phantom was animated using both sinusoidal and recorded patient-breathing traces. Fiducial were masked prior to 4DTDR for reconstruction. The original 4DCT data (with fiducials) were sampled into 20 CT phase sets and fiducials’ coordinates were recorded, resulting in time-resolved fiducial motion paths. Measured values of fiducial location were compared to EMT measured traces and the result calculated by 4DTDR.

Results: For the sinusoidal breathing trace, 95\% of EMT measured locations were within 1.2 mm of the measured 4DCT motion path, allowing for repeatable accurate motion characterization. The 4DTDR traces matched 95\% of the EMT trace within 1.6 mm. Using the more irregular (in amplitude and frequency) patient trace, 95\% of the EMT trace points fitted both 4DCT and 4DTDR motion path within 4.5 mm. The average match of the 4DTDR estimation of the tissue hysteresis over all CT phases was 0.9 mm using a sinusoidal signal for animation and 1.0 mm using the patient trace.

Conclusions: The real tissue phantom is a tool which can be used to accurately characterize tissue deformation, helping to validate or evaluate a DIR or 4DTDR algorithm over a complete motion path. The phantom is capable of validating, evaluating, and quantifying tissue hysteresis, thereby allowing for full motion path validation.

We discuss the use of stochastic collocation for the solution of optimal control problems which are constrained by stochastic partial differential equations (SPDE). Thereby the constraining, SPDE depends on data which is not deterministic but random. Assuming a deterministic control, randomness within the states of the input data will propagate to the states of the system. For the solution of SPDEs there has recently been an increasing effort in the development of efficient numerical schemes based upon the mathematical concept of generalized polynomial chaos. Modal-based stochastic Galerkin and nodal-based stochastic collocation versions of this methodology exist, both of which rely on a certain level of smoothness of the solution in the random space to yield accelerated convergence rates. In this paper we apply the stochastic collocation method to develop a gradient descent as well as a sequential quadratic program (SQP) for the minimization of objective functions constrained by an SPDE. The stochastic function involves several higher-order moments of the random states of the system as well as classical regularization of the control. In particular we discuss several objective functions of tracking type. Numerical examples are presented to demonstrate the performance of our new stochastic collocation minimization approach.

Keywords: stochastic collocation, optimal control, stochastic partial differential equations

We present a combinatorial algorithm for the general topological simplification of scalar fields on surfaces. Given a scalar field f, our algorithm generates a simplified field g that provably admits only critical points from a constrained subset of the singularities of f, while guaranteeing a small distance ||f - g||_∞ for data-fitting purpose. In contrast to previous algorithms, our approach is oblivious to the strategy used for selecting features of interest and allows critical points to be removed arbitrarily. When topological persistence is used to select the features of interest, our algorithm produces a standard ϵ-simplification. Our approach is based on a new iterative algorithm for the constrained reconstruction of sub- and sur-level sets. Extensive experiments show that the number of iterations required for our algorithm to converge is rarely greater than 2 and never greater than 5, yielding O(n log(n)) practical time performances. The algorithm handles triangulated surfaces with or without boundary and is robust to the presence of multi-saddles in the input. It is simple to implement, fast in practice and more general than previous techniques. Practically, our approach allows a user to arbitrarily simplify the topology of an input function and robustly generate the corresponding simplified function. An appealing application area of our algorithm is in scalar field design since it enables, without any threshold parameter, the robust pruning of topological noise as selected by the user. This is needed for example to get rid of inaccuracies introduced by numerical solvers, thereby providing topological guarantees needed for certified geometry processing. Experiments show this ability to eliminate numerical noise as well as validate the time efficiency and accuracy of our algorithm. We provide a lightweight C++ implementation as supplemental material that can be used for topological cleaning on surface meshes.

The Material Point Method (MPM) and the Implicit Continuous-fluid Eulerian method (ICE) have been used to simulate and solve many challenging problems in engineering applications, especially those involving large deformations in materials and multimaterial interactions. These methods were implemented within the Uintah Computational Framework (UCF) to simulate explosions, fires, and other fluids and fluid-structure interaction. For the purpose of knowing if the simulations represent the solutions of the actual mathematical models, it is important to fully understand the accuracy of these methods. At the time this research was initiated, there were hardly any error analysis being done on these two methods, though the range of their applications was impressive. This dissertation undertakes an analysis of the errors in computational properties of MPM and ICE in the context of model problems from compressible gas dynamics which are governed by the one-dimensional Euler system. The analysis for MPM includes the analysis of errors introduced when the information is projected from particles onto the grid and when the particles cross the grid cells. The analysis for ICE includes the analysis of spatial and temporal errors in the method, which can then be used to improve the method's accuracy in both space and time. The implementation of ICE in UCF, which is referred to as Production ICE, does not perform as well as many current methods for compressible flow problems governed by the one-dimensional Euler equations – which we know because the obtained numerical solutions exhibit unphysical oscillations and discrepancies in the shock speeds. By examining different choices in the implementation of ICE in this dissertation, we propose a method to eliminate the discrepancies and suppress the nonphysical oscillations in the numerical solutions of Production ICE – this improved Production ICE method (IMPICE) is extended to solve the multidimensional Euler equations. The discussion of the IMPICE method for multidimensional compressible flow problems includes the method’s detailed implementation and embedded boundary implementation. Finally, we propose a discrete adjoint-based approach to estimate the spatial and temporal errors in the numerical solutions obtained from IMPICE.

Area-preserving maps arise in the study of conservative dynamical systems describing a wide variety of physical phenomena, from the rotation of planets to the dynamics of a fluid. The visual inspection of these maps reveals a remarkable topological picture in which invariant manifolds form the fractal geometric scaffold of both quasi-periodic and chaotic regions. We discuss in this paper the visualization of such maps built upon these invariant manifolds. This approach is in stark contrast with the discrete Poincare plots that are typically used for the visual inspection of maps. We propose to that end several modified definitions of the finite-time Lyapunov exponents that we apply to reveal the underlying structure of the dynamics. We examine the impact of various parameters and the numerical aspects that pertain to the implementation of this method. We apply our technique to a standard analytical example and to a numerical simulation of magnetic confinement in a fusion reactor. In both cases our simple method is able to reveal salient structures across spatial scales and to yield expressive images across application domains.

The corpus callosum (CC) is a structure of interest in many neuroimaging studies of neuro-developmental pathology such as autism. It plays an integral role in relaying sensory, motor and cognitive information from homologous regions in both hemispheres.

We have developed a framework that allows automatic segmentation of the corpus callosum and its lobar subdivisions. Our approach employs constrained elastic deformation of exible Fourier contour model, and is an extension of Szekely's 2D Fourier descriptor based Active Shape Model. The shape and appearance model, derived from a large mixed population of 150+ subjects, is described with complex Fourier descriptors in a principal component shape space. Using MNI space aligned T1w MRI data, the CC segmentation is initialized on the mid-sagittal plane using the tissue segmentation. A multi-step optimization strategy, with two constrained steps and a final unconstrained step, is then applied. If needed, interactive segmentation can be performed via contour repulsion points. Lobar connectivity based parcellation of the corpus callosum can finally be computed via the use of a probabilistic CC subdivision model.

Our analysis framework has been integrated in an open-source, end-to-end application called CCSeg both with a command line and Qt-based graphical user interface (available on NITRC). A study has been performed to quantify the reliability of the semi-automatic segmentation on a small pediatric dataset. Using 5 subjects randomly segmented 3 times by two experts, the intra-class correlation coeficient showed a superb reliability (0.99). CCSeg is currently applied to a large longitudinal pediatric study of brain development in autism.

Quantitative analysis of early brain development through imaging is critical for identifying pathological development, which may in turn affect treatment procedures. We propose a framework for analyzing spatiotemporal patterns of brain maturation by quantifying intensity changes in longitudinal MR images. We use a measure of divergence between a pair of intensity distributions to study the changes that occur within specific regions, as well as between a pair of anatomical regions, over time. The change within a specific region is measured as the contrast between white matter and gray matter tissue belonging to that region. The change between a pair of regions is measured as the divergence between regional image appearances, summed over all tissue classes. We use kernel regression to integrate the temporal information across different subjects in a consistent manner. We applied our method on multimodal MRI data with T1-weighted (T1W) and T2-weighted (T2W) scans of each subject at the approximate ages of 6 months, 12 months, and 24 months. The results demonstrate that brain maturation begins at posterior regions and that frontal regions develop later, which matches previously published histological, qualitative and morphometric studies. Our multimodal analysis also confirms that T1W and T2W modalities capture different properties of the maturation process, a phenomena referred to as T2 time lag compared to T1. The proposed method has potential for analyzing regional growth patterns across different populations and for isolating specific critical maturation phases in different MR modalities.

2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.

Keywords: scidac

2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.