Carter Emmart used to want to go to space. Now he does, all the time — but virtually. And he likes to share.
On a recent afternoon, he took a visitor at the Hayden Planetarium, where he works, on a kind of joyride across the known universe. The lights dim. The inverted bowl of the planetarium’s screen comes to life. But the familiar, insectlike projector that displays the stars and constellations is stowed under the floor. Instead, projectors are reconstructing images onto the half dome from a desktop computer.
So there’s Mars, filling the screen, its reddish desert revealed as a landscape of mountains and valleys that make the Grand Canyon look like a puny arroyo. Flying around, we take in the sights, the surroundings uncannily close and detailed, so that boulders only a few feet across can be discerned.
Machine learning could improve how doctors diagnose heart attacks
Wilson Good, Brian Zenger Article originally appears in Venturebeat
You're working in your house, going about your normal routine when suddenly the pain hits. Your chest starts to throb and your left arm begins to ache. Without hesitation, you rush to the hospital, dreading your worst fear has become a reality — you are having a heart attack. Upon arrival, physicians, nurses, and other medical staff begin frantically testing, probing, and prodding nearly every part of your body. They run more tests than you can keep track of and begin shouting orders for new tests and other members of the team. The emergency physician is carefully watching the monitors hooked up by your bedside, puzzled by the results they are seeing. They turn to consult a cardiologist expert on the signals your heart is emitting. But instead of a person, they turn to a computer.
New research being conducted at the Scientific Computing and Imaging Institute holds the potential to increase the accuracy of targeted treatments for tumors in the lungs. Currently, motion caused by the patient's breathing introduces motion artifacts when imaging lung tumors. The inherent breathing motion also limits the precise targeting of radiation therapy for treating lung cancer.
"The only certainty...," it is said, "is that nothing is certain."
And so it goes with computational forecasts of important events such as weather, finance, and climate. Among all of this uncertainty, however, there are patterns, likelihoods, and rarities that inform important decisions that may affect billions of dollars in resources and thousands, or even millions, of lives. In the hurricane season on the eastern U.S., computational forecasting plays a central role in critical decisions that can determine allocations of emergency resources and the movements of people. The uncertainty and accuracy of these forecasts is an important part in making effective use of these sophisticated tools.
Whether it’s coming up with the best design for a Formula 1 race car or understanding the effects of atrial fibrillation on the heart, developing the right simulation model for research sometimes involves equal parts applied math, engineering and computer science.
University of Utah School of Computing professor Mike Kirby sees himself as the person who connects these disciplines so he can take trailblazing ideas and help create better simulation software to aid researchers.
For many who suffer from debilitating neurological disorders such as Parkinson’s Disease, the constant muscular tremors are an unbearable symptom. Just drinking from a cup can be an overwhelming challenge.
When medication doesn’t work, brain surgery to destroy certain cells can be risky, and the results are irreversible. But there has been an emerging third option — deep brain stimulation (DBS), a therapy in which electrodes are implanted in the patient’s brain that deliver continuous electrical pulses to control motor function.
University of Utah bioengineering associate professor Christopher Butson has been researching ways to improve DBS systems to make them more effective and convenient for patients who wear them. He believes an answer lies in mobile tablets and smartphones.
Most farmers probably never thought they'd be in the market for a way to process huge digital images more quickly -- until, that is, inexpensive drones with high-resolution cameras gave them access to images they could use to micromanage irrigation and to detect the growth of crop-threatening diseases.
Didactic lecture sessions given by the three PIs (Rob MacLeod, Ross Whitaker and Jeff Weiss) as well as three invited instructors (Miriah Meyer, Steve Maas and Gerard Ateshian) experts in their fields
Laboratory exercises lead by a group of teaching assistants and developers,
Discussion session time for student-instructor interaction,
Visit to the experimental and computational laboratory facilities at the University of Utah, College of Engineering to give the participants an overview of the general academic background and research projects performed at the university,
Four Keynotes Lectures from leaders in the field,
Mentoring lectures on grant writing, responsible conduct of research, and simulation study design.
Over the course of four weeks, students from West High School and Skyline High School and a homeschooled junior year student were given an introduction to image-based modeling (IBM) as part of the SCI Institute Summer Internship. This course, which is given to undergraduate students during the academic year, was customized to fit the high school students' level. The goal of this internship was to help the students understand how computational simulation is used in the biomedical field to improve our knowledge of the body, allowing researchers to collaborate with medical doctors to provide patient-specific treatment solutions.
Image Analysis Tools for Understanding Connective Tissue Structure
Sponsored by the Burton Foundation
This summer, two Salt Lake area high school students from Copper Hills High School came to the University of Utah to participate in a hands-on research experience. The students learned how image analysis tools help biomechanics researchers understand the effects of structural features of musculoskeletal tissues (e.g. tendons, ligaments, and articular cartilage) on the functional behavior of these tissues.
Many musculoskeletal tissue injuries and diseases exhibit altered macroscopic and microscopic tissue structure. The Musculoskeletal Research Laboratories, a research center of the Scientific Computing and Imaging Institute, uses engineered tissue materials to study the effect of these structural changes on tissue behavior. Researchers use many image acquisition techniques to characterize the structure of native and engineered tissues, including optical microscopy, x-ray computed tomography (CT), and electron microscopy. Image analysis tools allow efficient detection and quantification of structural features from these images.
On September 27th, Hurricane Joaquin, a Category 4 storm developed over the Atlantic Ocean pounding the Bahamas. There were a number of predications as to which way the storm would travel, one of which was that the hurricane would head north along the east coast of the United States, but the path of the storm changed direction and dissipated on October 7th.
The results denoted possible predicted paths, based upon different models and/or conditions Joaquin might take as of Friday October 2, 2015. Using their Curve Boxplot analysis and visualization method, they show the median hurricane path and the 50 percent band (dark region) — denoting the spatial swath in which 50 percent of the predicted hurricane tracks lie. The light band denotes nearly 100 percent of the possible paths predicted. Red denotes outliers — those hurricane paths flagged as unlikely in reference to all other members of the ensemble.
The two-week course included the following activities:
Didactic lecture sessions given by the three PIs as well as four invited instructors and experts in their fields .
Laboratory exercises led by a group of 10 teaching assistants and developers.
Discussion session time for student-instructor interaction.
A visit to the experimental and computational laboratory facilities at the University of Utah, College of Engineering to give the participants an overview of the general academic background and research projects performed at the university.
Mentoring lectures on grant writing, responsible conduct of research, and simulation study design.
The two-week summer course hosted 39 participants this year: 31 graduate students, 1 MD/PhD student, 2 postdoctoral fellows, 3 junior faculty, and 2 developers from a research laboratory / industry. Participants came from 24 institutions, including 4 from universities in Belgium and England. After the first week of common classes, participants were divided into two tracks: Bioelectricity (10 participants) and biomechanics (29 participants).
IBBM is a dedicated two-week course in the area of image-based modeling and simulation applied to bioelectricity and biomechanics, providing participants with training in the numerical methods, image analysis, visualization, and computational tools necessary to carry out end-to-end, image-based, subject-specific simulations in either bioelectricity or orthopedic biomechanics. The course focuses on using freely available, open-source software developed under the research of the CIBC (P41 GM103545) and FEBio suite (RO1 GM083925). Students use this software to learn and apply the complete dataflow pipeline to particular sets of data with specific goals.
This summer, high school students from the Salt Lake area are coming to the University of Utah to participate in hands-on research in image analysis of the human brain. In conjunction with graduate students and faculty in the School of Computing and the Scientific Computing and Imaging Institute, the students are learning how computer science can help neuroscience researchers understand the brain and disorders that affect it, such as Alzheimer's disease and Autism.
Advances in medical imaging devices, such as magnetic resonance imaging (MRI), have led to our ability to acquire detailed information about the living human brain, including its anatomical structure, function, and connectivity. However, making sense of this complex data is a difficult task, especially in large imaging studies that may include hundreds or even thousands of participants. This is where computer science can play an important role. Image analysis algorithms can automatically quantify properties of the brain, such as the size of brain structures, or the functional activity in different brain regions. This provides neuroscience researchers with insights into how the brain functions and what abnormalities are present in diseased brains.
There has been a recent explosion of interest in the use of noninvasive transcranial brain stimulation (or "neurostimulation"), both in clinical settings and as a research tool. One of the two main ways to stimulate the brain transcranially is to run a current to the brain through the magnetic fields generated by a coil that is held near the scalp. This approach has been approved by the FDA for treating depression. The other main approach uses electrodes placed on the scalp to "inject" current into the head, or apply voltage on the scalp, which is known as transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), depending on the type of current or voltage source used. Various neurostimulation technologies have been tested in human experiments for a huge variety of applications including motor rehabilitation, speech therapy, enhancement of cognitive learning, and treating depression and other affective and behavioral disorders, chronic pain syndrome, post-traumatic stress disorder, and others.
Transcranial Magnetic Stimulation (TMS) of the human motor cortex.
SCIRun is the integrated programming environment that has been a core technology of the CIBC since its inception and each major version release is an enormous undertaking. The program now contains hundreds of thousands of lines of C++ code and a new release requires at least a review of all this code, with replacement or updating of larger portions of it. We are nearing the first release of such a major new version, SCIRun 5.
There must be considerable motivation for such a major release, motivation which comes from both our users, collaborators, and DBP partners but also from advances in software engineering and scientific computing, with which we must also keep pace. Our users continue to demand more efficiency, more flexibility in programming the workflows created with SCIRun, more support for big data, and more transparent access to large compute resources when simulations exceed the useful capacity of local resources. The evolution of software engineering has led to changes in computer languages, programming paradigms, visualization hardware and processing, user interface design (and tools to support this critical component), and the third party libraries that form the building blocks of complex scientific software. SCIRun 5 is a response to all these changing conditions and needs and also represents some long awaited refactoring that will provide greater flexibility and freedom as we move into the next generation of scientific computing.
Partial differential equations (PDEs) are ubiquitous in engineering applications. They mathematically model natural phenomena such as heat conduction, diffusion, and shock wave propagation. They also describe many bioelectrical and biomechanical functions and are a central element of the simulation research of the Center. Analytical solutions for most PDEs are known only for certain symmetric domains, such as a circle, square, or sphere. In order to obtain solutions to PDEs for more realistic domains, numerical approximations such as the finite element method (FEM) are used. In the FEM, both the domain and the PDE are discretized and a numerical solution is calculated using computational resources. The discretization of the geometric domain is called a mesh. Meshes play a vital role in the numerical solution of PDEs on a given geometric domain, the accuracy of which depends on parameters such as the shape and size of the mesh elements. The most commonly used meshes contain tetrahedral elements. While simple conceptually, mesh generation is one of the most computationally intensive tasks in solving a PDE numerically.
Introducing ViSOAR. As data acquisition advances, and data sizes increase, the need for tools to process and visualize the results in an effective and efficient manner is becoming increasingly important. The reliance on supercomputers for scientific visualization and analysis is already proving to be a hindrance for wide accessibility to researchers and scientists dealing with large data.
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.
Cam Femoroacetabular Impingement Analysis using Statistical Shape Modeling
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.