SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP we introduce specially-designed streaming algorithms and data structures that provide an optimal solution for out-of-core multi-image processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

Articular cartilage experiences significant mechanical loads during daily activities. Healthy cartilage provides the capacity for load bearing and regulates the mechanobiological processes for tissue development, maintenance, and repair. Experimental studies at multiple scales have provided a fundamental understanding of macroscopic mechanical function, evaluation of the micromechanical environment of chondrocytes, and the foundations for mechanobiological response. In addition, computational models of cartilage have offered a concise description of experimental data at many spatial levels under healthy and diseased conditions, and have served to generate hypotheses for the mechanical and biological function. Further, modeling and simulation provides a platform for predictive risk assessment, management of dysfunction, as well as a means to relate multiple spatial scales. Simulation-based investigation of cartilage comes with many challenges including both the computational burden and often insufficient availability of data for model development and validation. This review outlines recent modeling and simulation approaches to understand cartilage function from a mechanical systems perspective, and illustrates pathways to associate mechanics with biological function. Computational representations at single scales are provided from the body down to the microstructure, along with attempts to explore multiscale mechanisms of load sharing that dictate the mechanical environment of the cartilage and chondrocytes.

Background and purpose
Acetabular retroversion may result in anterior acetabular over-coverage and posterior deficiency. It is unclear how standard radiographic measures of retroversion relate to measurements from 3D models, generated from volumetric CT data. We sought to: (1) compare 2D radiographic measurements between patients with acetabular retroversion and normal control subjects, (2) compare 3D measurements of total and regional femoral head coverage between patients and controls, and (3) quantify relationships between radiographic measurements of acetabular retroversion to total and regional coverage of the femoral head.

Patients and methods
For 16 patients and 18 controls we measured the extrusion index, crossover ratio, acetabular angle, acetabular index, lateral center edge angle, and a new measurement termed the "posterior wall distance". 3D femoral coverage was determined from volumetric CT data using objectively defined acetabular rim projections, head-neck junctions, and 4 anatomic regions. For radiographic measurements, intra-observer and inter-observer reliabilities were evaluated and associations between 2D radiographic and 3D model-based measures were determined.

Results
Compared to control subjects, patients with acetabular retroversion had a negative posterior wall distance, increased extrusion index, and smaller lateral center edge angle. Differences in the acetabular index between groups approached statistical significance. The acetabular angle was similar between groups. Acetabular retroversion was associated with a slight but statistically significant increase in anterior acetabular coverage, especially in the anterolateral region. Retroverted hips had substantially less posterior coverage, especially in the posterolateral region.

Interpretation
We found that a number of 2D radiographic measures of acetabular morphology were correlated with 3D model-based measures of total and regional femoral head coverage. These correlations may be used to assist in the diagnosis of retroversion and for preoperative planning.

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may cause shearing between the femur and acetabulum, leading to intraarticular damage and early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI, but provide only a planar view of the femoral head and often assume the ideal femur shape to be spherical. Statistical shape modeling (SSM) can be used to objectively compare complex 3D morphology without the need to assume ideal geometry. The objective of this study was to generate accurate 3D reconstructions of femoral heads and apply statistical shape modeling to quantify 3D variation and morphologic differences between control and cam femurs. Femurs from 33 controls and 15 cam FAI patients were CT scanned and 3D surfaces were generated by image segmentation. Correspondence particles were optimally positioned upon each surface using a gradient descent energy function. Resulting particle configurations were used to generate mean shapes for each group. Morphological differences were calculated as the distance between mean control and patient geometries. Differences were consistent with the location and approximate shape of cam lesions found intra-operatively. Deviations in mean shape between groups were pronounced at the anterolateral headneck junction, where the mean cam femur protruded from the mean control femur by a maximum of 2.7mm. Sustained protrusions of ~1.0-2.5mm were distributed from the anterior-posterior midline of the femoral neck along the entire anterolateral head-neck junction and distally along the anterior section of the neck. Future work will refine our statistical shape modeling software to quantify, on a patient-specific basis, the severity of cam lesions for pre-operative planning.

Our objectives were to determine cartilage contact stress during walking, stair climbing, and descending stairs in a well-defined group of normal volunteers and to assess variations in contact stress and area among subjects and across loading scenarios. Ten volunteers without history of hip pain or disease with normal lateral center-edge angle and acetabular index were selected. Computed tomography imaging with contrast was performed on one hip. Bone and cartilage surfaces were segmented from volumetric image data, and subject-specific finite element models were constructed and analyzed using a validated protocol. Acetabular contact stress and area were determined for seven activities. Peak stress ranged from 7.52 ± 2.11 MPa for heel-strike during walking (233% BW) to 8.66 ± 3.01 MPa for heel-strike during descending stairs (261% BW). Average contact area across all activities was 34% of the surface area of the acetabular cartilage. The distribution of contact stress was highly non-uniform, and more variability occurred among subjects for a given activity than among activities for a single subject. The magnitude and area of contact stress were consistent between activities, although inter-activity shifts in contact pattern were found as the direction of loading changed. Relatively small incongruencies between the femoral and acetabular cartilage had a large effect on the contact stresses. These effects tended to persist across all simulated activities. These results demonstrate the diversity and trends in cartilage contact stress in healthy hips during activities of daily living and provide a basis for future comparisons between normal and pathologic hips.

Keywords: hip, finite element, biomechanics, cartilage contact stresses, cartilage pressure

Objective: Brain enlargement has been observed in individuals with autism as early as two years of age. Studies using head circumference suggest that brain enlargement is a postnatal event that occurs around the latter part of the first year. To date, no brain imaging studies have systematically examined the period prior to age two. In this study we examine MRI brain volume in six month olds at high familial risk for autism.

Method: The Infant Brain Imaging Study (IBIS) is a longitudinal imaging study of infants at high risk for autism. This cross-sectional analysis examines brain volumes at six months of age, in high risk infants (N=98) in comparison to infants without family members with autism (low risk) (N=36). MRI scans are also examined for radiologic abnormalities.

Results: No group differences were observed for intracranial cerebrum, cerebellum, lateral ventricle volumes, or head circumference.

Conclusions: We did not observe significant group differences for head circumference, brain volume, or abnormalities of radiologic findings in a sample of 6 month old infants at highrisk for autism. We are unable to conclude that these changes are not present in infants who later go on to receive a diagnosis of autism, but rather that they were not detected in a large group at high familial risk. Future longitudinal studies of the IBIS sample will examine whether brain volume may differ in those infants who go onto develop autism, estimating that approximately 20\% of this sample may be diagnosed with an autism spectrum disorder at age two.

Background
No clear recommendations exist regarding optimal humeral component version and deltoid tension in reverse total shoulder arthroplasty (TSA).

Materials and methods
A biomechanical shoulder simulator tested humeral versions (0°, 10°, 20° retroversion) and implant thicknesses (-3, 0, +3 mm from baseline) after reverse TSA in human cadavers. Abduction and external rotation ranges of motion as well as abduction and dislocation forces were quantified for native arms and arms implanted with 9 combinations of humeral version and implant thickness.

Results
Resting abduction angles increased significantly (up to 30°) after reverse TSA compared with native shoulders. With constant posterior cuff loads, native arms externally rotated 20°, whereas no external rotation occurred in implanted arms (20° net internal rotation). Humeral version did not affect rotational range of motion but did alter resting abduction. Abduction forces decreased 30% vs native shoulders but did not change when version or implant thickness was altered. Humeral center of rotation was shifted 17 mm medially and 12 mm inferiorly after implantation. The force required for lateral dislocation was 60% less than anterior and was not affected by implant thickness or version.

Conclusion
Reverse TSA reduced abduction forces compared with native shoulders and resulted in limited external rotation and abduction ranges of motion. Because abduction force was reduced for all implants, the choice of humeral version and implant thickness should focus on range of motion. Lateral dislocation forces were less than anterior forces; thus, levering and inferior/posterior impingement may be a more probable basis for dislocation (laterally) than anteriorly directed forces.

Keywords: Shoulder, reverse arthroplasty, deltoid tension, humeral version, biomechanical simulator

Background
Lateral offset center of rotation (COR) reduces the incidence of scapular notching and potentially increases external rotation range of motion (ROM) after reverse total shoulder arthroplasty (rTSA). The purpose of this study was to determine the biomechanical effects of changing COR on abduction and external rotation ROM, deltoid abduction force, and joint stability.

Materials and methods
A biomechanical shoulder simulator tested cadaveric shoulders before and after rTSA. Spacers shifted the COR laterally from baseline rTSA by 5, 10, and 15 mm. Outcome measures of resting abduction and external rotation ROM, and abduction and dislocation (lateral and anterior) forces were recorded.

Results
Resting abduction increased 20° vs native shoulders and was unaffected by COR lateralization. External rotation decreased after rTSA and was unaffected by COR lateralization. The deltoid force required for abduction significantly decreased 25% from native to baseline rTSA. COR lateralization progressively eliminated this mechanical advantage. Lateral dislocation required significantly less force than anterior dislocation after rTSA, and both dislocation forces increased with lateralization of the COR.

Conclusion
COR lateralization had no influence on ROM (adduction or external rotation) but significantly increased abduction and dislocation forces. This suggests the lower incidence of scapular notching may not be related to the amount of adduction deficit after lateral offset rTSA but may arise from limited impingement of the humeral component on the lateral scapula due to a change in joint geometry. Lateralization provides the benefit of increased joint stability, but at the cost of increasing deltoid abduction forces.

Keywords: Shoulder simulator, reverse arthroplasty, lateral offset, center of rotation

In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.

In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships among the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.

Expression analysis of ~omics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.

We propose a metamorphic geodesic regression approach approximating spatial transformations for image time-series while simultaneously accounting for intensity changes. Such changes occur for example in magnetic resonance imaging (MRI) studies of the developing brain due to myelination. To simplify computations we propose an approximate metamorphic geodesic regression formulation that only requires pairwise computations of image metamorphoses. The approximated solution is an appropriately weighted average of initial momenta. To obtain initial momenta reliably, we develop a shooting method for image metamorphosis.

Image-space line integral convolution (LIC) is a popular approach for visualizing surface vector fields due to its simplicity and high efficiency. To avoid inconsistencies or color blur during the user interactions in the image-space approach, some methods use surface parameterization or 3D volume texture for the effect of smooth transition, which often require expensive computational or memory cost. Furthermore, those methods cannot achieve consistent LIC results in both granularity and color distribution on different scales.

This paper introduces a novel image-space LIC for surface flows that preserves the texture coherence during user interactions. To make the noise textures under different viewpoints coherent, we propose a simple texture mapping technique that is local, robust and effective. Meanwhile, our approach pre-computes a sequence of mipmap noise textures in a coarse-to-fine manner, leading to consistent transition when the model is zoomed. Prior to perform LIC in the image space, the mipmap noise textures are mapped onto each triangle with randomly assigned texture coordinates. Then, a standard image-space LIC based on the projected vector fields is performed to generate the flow texture. The proposed approach is simple and very suitable for GPU acceleration. Our implementation demonstrates consistent and highly efficient LIC visualization on a variety of datasets.

The primary goal of cartilage tissue engineering is to recapitulate the functional properties and structural features of native articular cartilage. While there has been some success in generating near-native compressive properties, the tensile properties of cell-seeded constructs remain poor, and key features of cartilage, including inhomogeneity and anisotropy, are generally absent in these engineered constructs. Therefore, in an attempt to instill these hallmark properties of cartilage in engineered cell-seeded constructs, we designed and characterized a novel sliding contact bioreactor to recapitulate the mechanical stimuli arising from physiologic joint loading (two contacting cartilage layers). Finite element modeling of this bioreactor system showed that tensile strains were direction-dependent, while both tensile strains and fluid motion were depth-dependent and highest in the region closest to the contact surface. Short-term sliding contact of mesenchymal stem cell (MSC)-seeded agarose improved chondrogenic gene expression in a manner dependent on both the axial strain applied and transforming growth factor-? supplementation. Using the optimized loading parameters derived from these short-term studies, long-term sliding contact was applied to MSC-seeded agarose constructs for 21 d. After 21 d, sliding contact significantly improved the tensile properties of MSC-seeded constructs and elicited alterations in type II collagen and proteoglycan accumulation as a function of depth; staining for these matrix molecules showed intense localization in the surface regions. These findings point to the potential of sliding contact to produce engineered cartilage constructs that begin to recapitulate the complex mechanical features of the native tissue.

The Uintah Computational 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. Uintah demonstrates excellent weak and strong scalability at full machine capacity on XSEDE resources such as Ranger and Kraken, and through the use of a hybrid memory approach based on a combination of MPI and Pthreads, Uintah now runs on up to 262k cores on the DOE Jaguar system. In order to extend Uintah to heterogeneous systems, with ever-increasing CPU core counts and additional onnode GPUs, a new dynamic CPU-GPU task scheduler is designed and evaluated in this study. This new scheduler enables Uintah to fully exploit these architectures with support for asynchronous, outof- order scheduling of both CPU and GPU computational tasks. A new runtime system has also been implemented with an added multi-stage queuing architecture for efficient scheduling of CPU and GPU tasks. This new runtime system automatically handles the details of asynchronous memory copies to and from the GPU and introduces a novel method of pre-fetching and preparing GPU memory prior to GPU task execution. In this study this new design is examined in the context of a developing, hierarchical GPUbased ray tracing radiation transport model that provides Uintah with additional capabilities for heat transfer and electromagnetic wave propagation. The capabilities of this new scheduler design are tested by running at large scale on the modern heterogeneous systems, Keeneland and TitanDev, with up to 360 and 960 GPUs respectively. On these systems, we demonstrate significant speedups per GPU against a standard CPU core for our radiation problem.

Keywords: csafe, uintah

The Uintah Computational 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. Uintah demonstrates excellent weak and strong scalability at full machine capacity on XSEDE resources such as Ranger and Kraken, and through the use of a hybrid memory approach based on a combination of MPI and Pthreads, Uintah now runs on up to 262k cores on the DOE Jaguar system. In order to extend Uintah to heterogeneous systems, with ever-increasing CPU core counts and additional onnode GPUs, a new dynamic CPU-GPU task scheduler is designed and evaluated in this study. This new scheduler enables Uintah to fully exploit these architectures with support for asynchronous, outof- order scheduling of both CPU and GPU computational tasks. A new runtime system has also been implemented with an added multi-stage queuing architecture for efficient scheduling of CPU and GPU tasks. This new runtime system automatically handles the details of asynchronous memory copies to and from the GPU and introduces a novel method of pre-fetching and preparing GPU memory prior to GPU task execution. In this study this new design is examined in the context of a developing, hierarchical GPUbased ray tracing radiation transport model that provides Uintah with additional capabilities for heat transfer and electromagnetic wave propagation. The capabilities of this new scheduler design are tested by running at large scale on the modern heterogeneous systems, Keeneland and TitanDev, with up to 360 and 960 GPUs respectively. On these systems, we demonstrate significant speedups per GPU against a standard CPU core for our radiation problem.

Keywords: Uintah, hybrid parallelism, scalability, parallel, adaptive, GPU, heterogeneous systems, Keeneland, TitanDev

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. Agraphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI]related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the communityfs attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a technique used to study and track contrast kinetics in an area of interest in the body over time. Reconstruction of images with high contrast and sharp edges from undersampled data is a challenge. While good results have been reported using a radial acquisition and a spatiotemporal constrained reconstruction (STCR) method, we propose improvements from using spatially adaptive weighting and an additional edge-based constraint. The new method uses intensity gradients from a sliding window reference image to improve the sharpness of edges in the reconstructed image. The method was tested on eight radial cardiac perfusion data sets with 24 rays and compared to the STCR method. The reconstructions showed that the new method, termed edge-enhanced spatiotemporal constrained reconstruction, was able to reconstruct images with sharper edges, and there were a 36\%±13.7\% increase in contrast-to-noise ratio and a 24\%±11\% increase in contrast near the edges when compared to STCR. The novelty of this paper is the combination of spatially adaptive weighting for spatial total variation (TV) constraint along with a gradient matching term to improve the sharpness of edges. The edge map from a reference image allows the reconstruction to trade-off between TV and edge enhancement, depending on the spatially varying weighting provided by the edge map.

Keywords: MRI, Reconstruction, Edge enhanced, Compressed sensing, Regularization, Cardiac perfusion