Imaging & Computational Modeling
Pediatric Craniofacial Surgery
Defining the Mechanical and Biologic Properties of Pediatric Cranial Bone
Jonathan Black, Assistant Professor, Plastic and Maxillofacial Surgery (UVA School of Medicine), Matt Panzer, Associate Professor, Mechanical & Aerospace Engineering (UVA Engineering), Patrick Cottler, Assistant Professor, Plastic & Maxillofacial Surgery (UVA School of Medicine)
Traumatic injury and congenital birth defects in infancy and childhood affecting the skull and facial bones usually require surgical correction. While it is known that the properties of the pediatric skull are substantially different from adult tissue, the composition, rigidity and structure are poorly studied and treatment devices are simply smaller versions of adult hardware. Additionally, most congenital corrections require expansion of the skull in order to allow growth, and there are few good tools to guide these procedures. This team from Plastic and Maxillofacial Surgery at the UVA School of Medicine and the Center for Applied Biomechanics will apply techniques and knowledge from the study of head and brain injury during automobile crashes and sports impacts to develop models based on pediatric cranial bone for surgical planning and simulation. This will lead to improved treatment and outcomes in pediatric craniofacial surgery.
Predictive Models of Carcinoma Cell Delamination from Heterogeneous Populations of Epithelial and Mesenchymal Cells
Matthew Lazzara, Associate Professor, Chemical Engineering (UVA Engineering), Shayn Peirce-Cottler, Professor, Biomedical Engineering (UVA School of Medicine), Dr.Todd Bauer, Professor, Surgery (UVA School of Medicine)
Over 50,000 patients are diagnosed every year with pancreatic ductal carcinoma (PDAC), which has a 5-year survival rate of only 8%. One of the main challenges in treating PDAC is that the cancer has already metastasized to other locations in more than half of PDAC patients at the time of diagnosis. Understanding the cellular interactions and behaviors that lead to metastases could lead to improved treatment options for these patients. In this project, experts from the Departments of Chemical Engineering, Biomedical Engineering, and Surgery are using computational models of tumor cell behavior and interactions in conjunction with experiments to better understand what drives metastasis and how to stop it.
Using Artificial Intelligence to Improve Brain Tumor Diagnosis, Prognosis and Treatment
Craig Meyer, Professor, Biomedical Engineering (UVA School of Medicine), Dr. Sohil Patel, Assistant Professor, Radiology & Medical Imaging (UVA School of Medicine), Xue Feng, Research Associate, Radiology & Medical Imaging (UVA School of Medicine), Nicholas Tustison, Associate Professor, Radiology & Medical Imaging (UVA School of Medicine), Thomas Fletcher, Associate Professor, Electrical & Computer Engineering (UVA Engineering)
Artificial intelligence is rapidly changing the way we analyze and interpret medical images. A team from the University of Virginia recently won an international machine learning competition on recognizing brain tumors and predicting patient outcomes based on MRI scans. In this project, the team will apply their award-winning algorithms to recognizing and planning treatment for gliomas, one of the most common types of brain tumor. This team brings together investigators from the Departments of Biomedical Engineering, Radiology & Medical Imaging and Electrical and Computer Science to combine machine learning and statistical expertise with expertise in the physics and clinical use of MRI. They aim to not only improve automated detection of tumors on MRI, but also combine image data with genomic and other information to improve predictions for individual patients and better guide their treatment.
Imaging Seizure and Memory Engrams
Daniel Weller, Assistant Professor, Electrical & Computer Engineering (UVA Engineering), Jaideep Kapur, Professor, Neurology (UVA School of Medicine), Cedric Williams, Professor, Psychology (CLAS)
Currently, three million Americans suffer from epilepsy, a condition that is characterized by recurrent spontaneous seizures. These seizures interfere with memory formation, explaining why poor memory is the next most common problem faced by patients with epilepsy. Despite many years of research, how seizures interfere with memory recall and formation remains unclear, partly due to the limits of existing measurement tools and analysis techniques. The University of Virginia project team of an electrical engineer, a neurologist and a behavioral neuroscientist will pioneer new methods for mapping activated neurons during short term memory formation in order to compare memory formation and seizure pathways
A New Engineering Protocol for Diagnosis and Treatment of Voice Disorders
An estimated 7.5 million people in the United States have a voice disorder, one-third of which can be attributed to vocal cord paralysis or weakness. Diagnosis and treatment options are limited by the fact that characterization of voice conditions requires an understanding of a patient’s specific anatomy and how air flows through their larynx – a problem that could be solved by combining CT imaging and computer modeling. The University of Virginia is uniquely positioned to study and impact treatment for this population with a research team comprised of a voice disorder expert from the Department of Otolaryngology and an engineer who studies flow dynamics in the Department of Mechanical and Aerospace Engineering.
Fast and Automatic Reconstruction of High Frame-Rate Cardiac Magnetic Resonance
DANIEL WELLER, Assistant Professor Electrical and Computer Engineering (UVA Engineering), CHRISTOPHER KRAMER, Professor Medicine – Cardiovascular Medicine and Radiology (UVA School of Medicine), MICHAEL SALERNO, Associate Professor, Medicine – Cardiovascular Medicine (UVA School of Medicine)
Heart disease accounts for a large fraction of deaths and hospitalizations in the United States. Emerging new cardiac magnetic resonance imaging (CMR) techniques have the potential to improve both diagnosis and management of heart disease, but these new techniques often require intensive data processing that delays scan results and discourages routine clinical use.
In this project, engineers from UVA’s DEPARTMENT FOR ELECTRICAL AND COMPUTER ENGINEERING and doctors from UVA’s DEPARTMENT OF CARDIOVASCULAR MEDICINE and RADIOLOGY will collaborate to develop and test fast, automated algorithms for processing high-resolution CMR. The ultimate goal of their work is to enable the widespread use of sophisticated imaging techniques that are currently only available at academic centers such as UVA.
In situ Bioengineering of Scar Formation after Myocardial Infarction
BRENT FRENCH, Professor Biomedical Engineering (UVA School of Medicine), JEFF SAUCERMAN, Associate Professor Biomedical Engineering (UVA School of Medicine), MATTHEW WOLF, Associate Professor Medicine – Cardiovascular Medicine (UVA School of Medicine)
Myocardial infarction (MI), or heart attack, occurs in approximately 800,000 people in the United States every year. One of the most successful therapies following a heart attack is called reperfusion therapy, which brings blood flow back to the region of the heart that has been injured by MI. Besides restoring blood flow to oxygen-starved heart muscle, reperfusion also improves clinical outcomes by expediting the replacement of dead heart muscle with scar tissue after MI.
In this project, bioengineers from UVA’s Department of Biomedical Engineering and a cardiologist from UVA’s Department of Medicine will take a highly innovative approach that harnesses two technologies developed at UVA to design and test new therapies to further improve the wound healing response in the heart after MI. The team will use computational models of the complex biology of cardiac fibroblasts to identify specific proteins inside those cells that might be modulated to improve the healing response, and then test their predictions by using viral gene delivery to regulate the levels of these proteins in animal models.
Precision mapping for Discovery and Development of Personalized Therapy for CVD: Role of Id3 and rs11574 in VSMCs
Scientists often find clues to the causes of a disease or condition such as high blood pressure by identifying single nucleotide polymorphisms (SNPs), minor genetic variations that are associated with that condition across a large number of people. For this project, partners from the UVA Department of Medicine and Department of Biomedical Engineering will use novel techniques for mapping how individual cells mature and develop to understand how a specific mutation identified in patients leads to coronary artery disease. Their studies may reveal new pathways regulating the onset of cardiovascular disease and may provide novel insights into individualized therapeutic strategies based on patient genotype.