SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

Created: 11 June 2012

by Gregory Scott Jones - NICS

The crater resulting from the Spanish Fork Detonation.

First, the bad news: all across America, trucks and tractor-trailers are transporting industrial explosives on nearly every artery of the country's interstate and highway system. That's right, volatile explosives, including munitions, rocket motors, and dynamite, are moving at a high rate of speed down a roadway not too far from you.

Now, the good news: America's track record in transporting these materials is about as safe as they come. Very rarely, almost never in fact, are the potential dangers of these transports realized, largely due to instituted safeguards that seem to work very well.

However, accidents can happen. Take the August 2005 incident in Spanish Fork Canyon, Utah, for instance. A truck carrying 35,500 pounds of explosives—specifically small boosters used in seismic testing—overturned and exploded, creating a crater in the highway estimated to be between 20 to 35 feet deep and 70 feet wide according to the Utah Department of Transportation. But the damage wasn't solely financial. Four people, including the truck driver and a passenger, were hospitalized.

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Created: 19 December 2013

In collaboration with Dr. Don Tucker and his colleagues at Electrical Geodesics Inc (EGI) and the University of Oregon, this DBP is concerned with improving our ability to reconstruct and visualize neuroelectric sources (source localization) from EEG measurements and also our ability to stimulate specific brain regions using electrodes attached on to the scalp of the subject (transcranial direct current stimulation, tDCS).

For both research and clinical practice, EEG is a cost-effective tool to understand and excite brain activity. EEG advances have significantly improved the spatial resolution of source estimates and offer the promise of precise spatio-temporal monitoring and stimulation of cortical brain activity. By itself, high-resolution EEG would be affordable even for small hospitals in remote locations and could be easily managed by technicians in the field.

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Created: 18 December 2013

Femoroacetabular impingement (FAI) is caused by reduced clearance between the femoral head and acetabulum due to anatomic abnormalities of the femur (cam FAI), acetabulum (pincer FAI), or both (mixed FAI). Cam FAI is characterized by an aspherical femoral head or reduced femoral head-neck offset. During hip flexion, the abnormally shaped femur may cause shearing at the chondrolabral junction, thereby damaging articular cartilage and the acetabular labrum. Currently, diagnosis of cam FAI is largely accomplished using two-dimensional (2D) measurements of femur morphology acquired from radiographic projections or a series of radial planes from computed tomography (CT) or magnetic resonance (MR) images. Two- dimensional measures provide initial diagnosis of cam FAI, but their reliability has been debated. Also, there is no agreement on the range of measurements that should be considered normal. Furthermore, radiographic measures give only a limited description of femur anatomy or shape variation among cam FAI deformities. Together, these limitations of 2D measurements translate into a high misdiagnosis rate. In a series of FAI patients treated with surgery in our clinic, 40% had seen multiple previous musculoskeletal providers and 15% had undergone surgical procedures unrelated to the hip joint (hernia, etc.).

Mean control (left) and cam (right) shapes. Middle images show the mean control shape with color plots depicting how the mean cam shape differed across the femoral head, neck, and proximal shaft. Top and bottom rows show different rotations of the femoral head.	Volumetric CT images from a cam FAI patient. Validated threshold settings were applied to CT images to segment and reconstruct the bony morphology of each femur.

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Created: 17 December 2013

Corview screenshot

Atrial fibrillation (AF) is a cardiac rhythm disturbance in which the atria, the upper chambers of the heart, undergo uncontrolled and uncoordinated electrical activation so that contraction of the atria contributes almost nothing to cardiac output. While not immediately atal (as is ventricular fibrillation) AF dramatically increases the risk of stroke, elevates mortality, and diminishes quality of life. Traditional diagnosis of AF as been limited to ECG-based determination of the time spent in this arrhythmia and there has previously been no other dependable biomarker capable of determining either the progression of the disease or of determining suitable treatment approaches. Therapy for AF consists of either antiarrhythmic drugs that may control the arrhythmia completely or at least reduce the resulting elevated heart rate combined with anticoagulation therapy or ablation. Ablation involves destroying targeted regions of the atria with the goal of either isolated triggers of spurious electrical activity or functionally separating the atrial wall into small enough segments that the putative mechanism of the arrhythmia cannot longer sustain. The latter approach is a form of substrate stabilization, and the management of this disease has suffered from a persistent lack of means to monitor or evaluate the stability of the tissue. It is precisely in this aspect that cardiologist at the University of Utah, with support from the CIBC have made their most significant contributions.

An interdisciplinary team at the Comprehensive Arrhythmia and MAnagement (CARMA) Center have made use of the segmentation, image analysis, and recently mesh generation and simulation capabilities of the CIBC to create a comprehensive program for AF management. The scope of the progress continues to expand each year and this application of CIBC technology has proven very fruitful even as it is very challenging.

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Created: 16 December 2013

For years, a central focus of the Center for Integrative Biomedical Computing has been software dissemination. Over the Center's twelve-year history, the infrastructure of CIBC's software dissemination efforts have undergone a radical evolution, with many lessons learned for both users and developers.

Software Dissemination as a Form of Science and Technology Dissemination

The SCIRun/BioPSE Problem-Solving Environment. This system was designed as a 'computational workbench' and represented a new approach to bringing high-end computational tools to the biomedical researcher.

From the outset, the Center's leadership believed that along with the typical avenues of dissemination, publications, seminars, workshops, conference presentations, etc.‚ software dissemination held a particularly high potential as a means to disseminate the knowledge and advances of the Center and its collaborators. This fundamental belief led to experimentation in a variety of topics, such as software licensing, open source repositories, make systems, operating systems, source code/binary releases, research code versus releasable code, software support, and release schedules.

The origins of our success in developing widely used software tools lie in a set of strategies for algorithm research and software development. One such strategy is the production of software tools with low barriers to entry. This entails the release of documented, tested, complete applications that do not require learning new programming languages or complex, architecture-specific build environments. We also continue to follow an initiative to create a suite of lightweight, stand-alone applications, directed at specific tasks of common interest across a wide set of disciplines. The result is a set of programs, such as Seg3D, with large and growing user bases.

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Created: 16 December 2013

Overview of the DBS system. The DBS electrode is implanted in the brain during stereotactic surgery. The electrode is attached via an extension wire to the IPG, which is implanted in the torso. The entire system is subcutaneous and is designed to deliver continuous stimulation for several years at a time.

In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies, such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow is a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. Recently, the CIBC has worked with Dr. Christopher Butson at the University of Wisconsin to deploy the interactive visualization system ImageVis3D Mobile for experimental use in the area of DBS planning. In addition to running on multi-node compute clusters and large desktop systems, ImageVis3D is also designed for mobile computing devices such as the iPhone or iPad. In this case, ImageVis3D was modified for an evaluation environment in order to visualize models of Parkinson's Disease (PD) patients who received DBS therapy¹.

The selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. 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 (independent 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 care, but were selected in drastically less time. We found that our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision-making for selecting DBS settings. The positive impact of the system could also translate to areas other than DBS.

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Created: 16 December 2013

A cross-section of a 3-dimensional, tetrahedral mesh of a torso. Each separate organ type is shown using a different color.

This year, an essential goal has been to enhance the generalized image-processing pipeline of software developed by CIBC and its partners. With the growing use of high quality medical imaging, practitioners around the globe are employing these acquired datasets for performing biomedical simulation. In its holistic approach to image-to-simulation pipelines, our software starts with image data and processing, constructs geometric models, performs simulation, and provides biophysical analysis of the data. A research highlight for the CIBC this year is the development of an improved scheme for mesh generation, a critical step within this pipeline. This research complements the software package BioMesh3D, but targets a completely different niche in the world of mesh generation algorithms.

High Quality Meshing

The problem of mesh generation has been widely studied, as a hybrid field of interest to the scientific, engineering, and computer science communities. In each of these fields, meshes are used to compute numerical approximations to solutions of partial differential equations. To do so, continuous mathematics are replaced with a discrete analogue, most commonly to facilitate the finite element method (FEM).

The FEM works by decomposing a domain of interest into discrete entities of various dimensions, such as points (0-dimensional), edges (1-dimensional), and cells of higher dimension (frequently triangles and quadrilaterals are used for 2-dimensionl elements, tetrahedra and hexahedra for 3-dimensional). Together, these elements form what is commonly called a mesh (see figure)Solutions to the complex system are solved piecewise on each element, and then aggregated together to form the final solution. The FEM has become an important tool in medical imaging as well. For example, CT scans of legs can be meshed so that orthopedic modeling can accurately simulate gait, MRI scans of the torso are frequently used in cardiac electrophysical modeling, and images of the skull can identify structures of the brain.

Because the FEM is a computational tool that processes individual elements to approximate a whole solution, it is deeply impacted by the mesh elements used to represent the space. Two principle concerns stand out in the meshing problem for medical images:

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Created: 21 May 2012

Figure from R.S. MacLeod, et al., Subjectspecific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples. Example A particle system provides adaptive sampling the various material boundaries of a segmented CT volume from a human torso.

Over the past year the CIBC, in partnership with our collaborators, has begun to introduce a generalized image-processing pipeline and associated software to the biomedical community.

With the widespread use of medical imaging, there is a growing need for better analysis of datasets. One method for improving analysis is to simulate biological processes and medical interventions in silico, in order to render better predictions. For example, the CIBC center is currently collaborating with Dr. Triedman at Children's Hospital in Boston to develop a computer model that will help guide the implantation of Implantable Cardiac Defibrillators (ICDs). This model uses pediatric imaging to select placement of electrode leads to generate the optimal field for defibrillation. One of the critical pieces in the development of the model is the generation of quality meshes for electric field simulation. Because the project is entering the validation phase where many cases need to be reviewed, a robust and automated Meshing Pipeline is required.

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Created: 21 May 2012

Atrial fibrillation (AF) is an electrophysiological condition that represents an increasing problem in the aging populations of the world; AF doubles the risk of stroke and mortality and diminishes quality of life. The best current method to evaluate the progression of AF and monitor the success of interventions is via an invasive intra-cardiac catheter-based electrical mapping procedure. A noninvasive means to evaluate characteristics of AF prior to treatment and to track the effect of interventions over time would be extremely valuable, and magnetic resonance imaging (MRI) offers such an opportunity. Before MRI can achieve its potential, there are challenging technical problems to overcome, such as the high spatial resolution required to image the thin atrial wall and the temporal resolution and gating necessary to compensate for the distorting effects of respiratory and cardiac motion. The Comprehensive Arrhythmia Research and Management (CARMA) Center has become a world leader in the use of MRI in AF and has overcome many of the image acquisition hurdles to make MRI a standard component of AF patient management at our institution. These improvements in image acquisition have opened up significant opportunities and new questions for the understanding and clinical management of AF.

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Created: 21 May 2012

An Isosurface visualization of a magnetic resonance imaging data set (in orange) surrounded by a volume rendered region of low opacity (in green) to indicate uncertainty in surface position.

The estimation and visualization of uncertainty information is an important research problem in both simulation and visualization. Uncertainty is a term used to describe the error, confidence, and variation of a dataset in order to allow a scientist to understand the accuracy not only of the data but also of the processes used to explore the data. One such technique, sensitivity analysis, helps the scientist to understand the effects of perturbing parameters of a function. Small perturbations of the input parameters that create large perturbations in the output results can indicate areas of the function that are highly dependent on the input parameters and may be interpreted as unstable or possibly wrong. Sensitivity analysis techniques can be used not only to explore the mathematical models used to generate uncertainty data but also to better understand the effects of input parameters of visualization techniques. Uncertainty data generated from the analysis of a mathematical model reconstructing biological experiment have been a focus of the CIBC team.

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