The Academy's 2019 CECI2 Class
My ultimate research goal is to develop imaging tools that help in understanding and diagnosing psychiatric disease. Psychiatric disease affects almost one in five U.S. adults each year, with 4% experiencing serious functional impairment as a consequence. Brain imaging tools such as Magnetic Resonance Imaging (MRI) can provide precise diagnosis, thus improving psychiatric treatment. It is well established that Diffusion MRI is exquisitely sensitive to long-range axonal connections in the brain’s white matter and diffusion-derived brain connectivity has inspired a wide range of population level insights in psychiatric disease processes. To date however, no clinically useful markers have been found for patient diagnosis on an individual level. This absence of clear disease detection has hampered the evaluation of treatment protocols, prolonging each patient’s search for effective treatment. It is my aim to employ novel Diffusion MRI acquisition strategies and post-processing tools for diagnosing these patients.
My research program has developed imaging measures that characterize the brain tumor environment. Brain tumor cells influence other non-tumor cells around them to help them thrive. My research has furthered the understanding of these interactions and the effects of therapy upon the brain tumor. As a result, we can now better determine who will survive longer. This is particularly important for new therapies that act to unleash the patient’s immune system to fight the brain tumor. Using standard of care imaging methods, we cannot determine who will respond to immunotherapy. My lab has recently developed a new approach of imaging these immune cells in a way that is easily done in patients and captures the effectiveness of therapy. I hope to discuss the importance of these new imaging measures and the necessity of their use in clinical trials to specifically capture if the therapy is working.
My research interests focus on development and application of advanced MRI technology for: 1) improving diagnosis of musculoskeletal disease and 2) improving in vivo characterization musculoskeletal tissue function in health and disease. Current therapeutic advances to treat cartilage degeneration are limited by a lack of noninvasive imaging markers of cartilage health. We are developing and validating quantitative MRI approaches for detecting early degenerative changes within cartilage tissue to better monitor disease progression and therapeutic intervention. An additional area of work in my lab is the development and application of microvascular and metabolic function in skeletal muscle. Skeletal muscle is implicated in the progression and outcome a variety of diseases such as diabetes, kidney disease, and heart disease. We are actively developing and utilizing several noninvasive MRI tools to better understand and characterize physiological changes with different disease states to aid the development of therapeutic strategies.
If “a picture is worth a thousand words”, then a picture that can measure something is worth a thousand answers. This is the motto that drives my research in imaging science using magnetic resonance imaging (MRI). We usually think of imaging water with MRI, but sodium is also naturally magnetic and just as physiologically relevant. My central goal is to expand sodium MRI methods to better understand disease mechanisms involving sodium, a key player in lymphedema, lipedema, heart failure, chronic kidney disease, and hypertension. Over the past three years, I’ve become interested in the relationship between lymphatic circulation and how the body processes sodium. I’ve developed ways to visualize lymphatic vessels noninvasively using MR lymphangiography, and showed that lymphedema improves after common physical therapy. My ongoing research seeks to apply sodium and lymphatic imaging to vascular and cardiovascular diseases: lymphedema, lipedema, and salt sensitive blood pressure that leads to hypertension.
My research interests focus on the development and translation of quantitative imaging methods to better understand and characterize (patho-) physiologic processes, and to evaluate treatments in musculoskeletal diseases. Currently, through simulations, and high field imaging of 3D printed phantoms and animal models (validated by histology), we are evaluating the specific ways in which diffusion MRI data are sensitive to changes in muscle fiber size and composition. Translation of these methods could help to evaluate treatments for myopathies, or track recovery after orthopedic surgery.I’m also interested in developing methods to measure inflammation in skeletal muscle via 19F and 1H MRI, which will allow for early detection of both pathologic changes in many disease states and could help to identify early responses to therapy. Finally, I’m interested in studying blood flow and metabolism in muscle to better understand the ramifications of microvascular dysfunction in diabetes and cardiovascular disease.
Continuous blood flow to the brain is needed for neural tissues to survive, and has devastating consequnces when it is disrupted, most notably in acute stroke. My research goal is to develop novel imaging methods to measure CBF and oxygenation in brain tissues. The imaging biomarkers I develop are quantitatie and sensitive to different brain states (e.g., using vasodilation as a “stress test for the brain”) and diseases. Once validated, these biomarkers will likely play a critical role in multi-center trials of stroke, by identifying patients who are good candidates for new therapies (including a catheter-based treatments).
I am interested primarily in the diagnosis and treatment of cancers using radioactivity in the field of Theranostics. For example, we currently use a molecule which binds to a cell receptor which is overexpressed in a specific type of tumor known as a neuroendocrine tumor. We are able to label this molecule with a radioactive isotope which can be injected in the patient, travels to the specific cell type, and used to image the patient to see if the receptor for the molecule is being overexpressed in the tumor for which we are looking.
If we see the tumor on the images, we can remove the imaging radioisotope from the molecule, and replace it with a high energy isotope, which is not used for imaging, but is used for treatment.
This high energy isotope can only travel a few millimeters in tissue, so what happens is the molecule with the high energy is injected into the patient, the molecule travels through the body until it finds the tumor and binds to the overexpressed receptor. After it binds the receptors on the tumor, it releases the high energy radioactivity which can only travel a few millimeters, thus destroying the tumor without damaging the surrounding healthy tissues.
This technology is currently only FDA approved for use in rare neuroendocrine tumors and for treating thyroid cancer, and, as one could imagine, there are many other tumors we could use this technology to target.
My research interests center around developing novel technologies that make magnetic resonance imaging (MRI) more flexible and robust. MRI is an incredibly powerful tool for looking inside the human body, yet it is often limited by real-world challenges such as patient movement, hardware imperfections, and time restrictions.The goal of my work is to include the effects of these real-world problems into our understanding of MRI. This allows us to produce higher quality diagnostic images and makes MRI accessible to a wider range of patients (such as children and the elderly, who are likelier to move during MRI exams). However, adding in real-world effects presents large computational problems, taking days to run even on state-of-the-art computers. I use advanced mathematical techniques and artificial intelligence to break down these large problems, so they can instead take minutes or seconds to solve, allowing their use in a wide variety of clinical settings.
My long-term research interests are in radiogenomics and gene therapy. At present, the field of neurogenetics involves evaluation of genetic mutations and their effects on the central/peripheral nervous systems. Medical imaging and artificial intelligence will become increasingly important in this area by offering noninvasive data on organ structure/function, which can serve as biomarkers of disease severity, progression, and response to therapy. The emerging field of gene therapy harnesses genomic editing technology to treat debilitating and often rare disorders that have not previously had a cure. This heralds an exciting era of precision medicine, in which diagnosis, prognosis, and therapy are tailored to a patient’s radiogenomic signature.
My research aims to develop better imaging tools for detecting white matter damage in multiple sclerosis, the leading cause of non-traumatic disability in young adults. We use a custom-built MRI scanner developed for the Human Connectome Project to evaluate white matter damage, which will enable us to track disease progression and monitor treatment response better in patients with multiple sclerosis. We are also developing the next generation of MRI scanners for imaging the microscopic structure of the human brain, which is less understood than any other organ in the body. Together, this research will enable us to image the brain in states of health and disease at unprecedented resolution, providing scientific insights that will ultimately drive the discovery of treatments for neurological diseases that currently have no cure.
My lab develops computational imaging solutions for improved diagnosis and treatment of diseases. One research direction aims to develop methods for reducing radiation dose and costs for imaging procedures. For example, a widely used modality for diagnosing coronary artery disease, the leading cause of death in the USA, is single photon emission computed tomography (SPECT) imaging. Typically, a CT scan is acquired with the SPECT scan to compensate for clinically significant distortions in SPECT images. However, acquiring the CT image leads to increased radiation dose and costs, and potentially inaccurate diagnosis if the patient moved between the SPECT and CT scans. To address these issues, we are developing methods that could compensate for the distortions without using the CT scan. For this project, we recently received the NIBIB Trailblazer award for early-stage investigators.In another research direction, we are developing computational methods to obtain biomarkers from medical images with the goal of personalizing medicine in patients with cancer. In one project, we observed in a prospective clinical trial that a set of machine-learning-based methods we had developed to quantify metrics from diffusion MR images were important for predicting therapy response at an early stage in patients with liver metastasis. This is highly significant since early prediction can help adapt the therapy regimens for the patients, thus helping to reduce side-effects and therapy costs, and potentially yield improved clinical outcomes. Similarly, we recently developed artificial-intelligence (AI)-based methods to quantify metrics from positron emission tomography (PET) images of patients with lung cancer, again with the goal of predicting therapy response at an early stage and thus adapting therapy. Research along this direction will be the focus of my presentation at the Medical Imaging Technology Showcase.
Non-invasive detection of early signs of neurodegenerative disease and brain injury could provide a longer window of opportunity for intervention, improve patient’s quality of life, and reduce burden for caregivers. To this end, I have leveraged my educational background in mechanical engineering to develop neuroimaging tools to study infant brain development, traumatic brain injury and Alzheimer’s disease.
My overarching research focuses on improving delivery of high-quality, evidence-based care to patients undergoing breast imaging for cancer detection and evaluation, particularly amongst vulnerable and underserved populations.One potential topics of interest to discuss at congressional meetings is my ongoing research on the use of patient navigation programs to reduce barriers and disparities in access to breast imaging. Another is my work on optimizing imaging evaluation of women with signs and symptoms of breast cancer, both in the United States and internationally through global cancer collaborations. I have also led multi-institutional collaborative studies on the appropriateness of breast MRI reporting practices and diagnostic mammography reporting practices.
Prior to my clinical research in breast cancer imaging, much of my earlier research experiences were in the laboratory sciences in the field of molecular biology. This basic science research background helped foster my skills in scientific methodology and developed a strong foundation for my current clinical-translational research.
The basic design of the conventional x-ray tube has not significantly changed since its invention by William Coolidge in 1913. Almost all current diagnostic x-ray tubes utilize a heated tungsten filament to generate the electron stream necessary for x-ray production, which limits the ability to put multiple x-ray sources in close proximity and reduces the precise control of the source. A new x-ray tube based on carbon nanotubes was invented at the University of North Carolina at Chapel Hill in 2003. My research is focused on bringing x-ray sources based on this tecnology to a variety of clinical applications, including 3-D breast imaging, cardiac, chest and orthopedic imaging. The new x-ray source has the potential to reduce the radiation dose to patietns, provide higher quality images, and be used to develop portable imaging devices for the military and emergency medical services. The CNT sources serve as an example of the complexities in translating and adapting a novel imaging technology to the clinic.
Coming Soon.
My area of interest is understanding of the effects of lung diseases on the heart and the vessels in the body. I am interested in exploring the role of diagnostic tests (CT and MRI) to further our knowledge and eventually use that understanding to decrease the burden of cardiovascular death. Cardiovascular disease has been the most common cause of death for > 50 years. While smoking is declining with an aggressive campaign regarding its harmful effects, the use of e-cigarettes is on the rise, especially among young adults. The focus of my current research is to study the effects of e-cigarettes use on the heart and compare it with traditional smoking. To further our understanding of the disease, I am involved in the development of MRI sequences that enables its use without the need for breath-holding. Because of a need for breath-holding, many patients that need cardiac MRI cannot get the test. With such development, patients that have advanced lung disease can also be studied to understand the effects on the heart using imaging.
My primary research is in traumatic brain injury (TBI) for which I have multiple studies. First, I am working to develop and test novel MRI methods to better detect the subtle, but devastating, brain injuries that occur after concussion. One of these promising methods is diffusion tensor imaging (DTI) which is challenging to interpret. I have an accompanying innovation study to develop a robust method to analyze DTI which has resulted in 2 inventions. I am also develop a new concussion management scheme aimed at improving care while decreasing healthcare costs. My other major research focus is Alzheimer’s Disease for which I am developing a prognostic method to determine rate of neurodegenerative and cognitive decline. I would like to discuss the impact of TBI and Alzheimer’s disease and the potential imaging research has to aid in better diagnosis and treatment tools for these conditions.
• Microstructural and Functional Connectome Correlates of AutismIn these research projects, I have applied advance imaging techniques and postprocessing analysis to examine the details of brain connectivity in children with neurodevelopmental disorders, including autism. We have used a special type of MRI (called DTI) to assess nerve fibers as conduits of connectivity in the brain at a microstructural level. We have also used “functional” MRI studies to look at synchronicity of fluctuations in the blood oxygen levels at different areas of the brain as a measure of activity pairing – or “functional connectivity”. Together these data provide complementary, but independent measures of brain organization and connectivity – i.e. Connectome. In the next step of our research, we will apply machine learning models to incorporate information from both these MRI techniques into one framework to devise novel quantitative biomarkers, which could guide risk stratification, prognostication, and treatment planning in children with autism.
• Automated Assessment of Stroke on CT Scan using Artificial Intelligence
Availability of mega-data and machine-learning algorithms have paved the road for development of novel diagnostic and prognostic solutions in medical imaging. The Yale New Haven Health system is the largest health care delivery system in Connecticut, including four hospitals (Yale New Haven, Bridgeport, Greenwich, Lawrence &Memorial) with 2409 beds and over two million inpatient admissions per year. The Yale Stroke Center is the lead hub of the Southern New England Partnership in Stroke Research, Innovation and Treatment (SPIRIT) of the NIH StrokeNet with approximately 1,800-2,000 code stroke per year. The centralized Image Archiving System and Electronic Medical Record system have provided a unique opportunity for collecting the mega-data necessary for machine learning models.
Our research group is applying artificial intelligence algorithms for automated detection of stroke on head CT scan and measuring the amount of tissue damage surrounding a brain bleed. Such automated tools can help with triage and diagnosis of critical findings such as acute stroke, which may be particularly important in resource-challenged parts of the country such as smaller, rural communities without access to subspecialty trained radiologists.
• Risk Stratification and Treatment Planning for Patients with Brain and Neck Tumor using Machine-Learning Quantitative Assessment of Medical Images
In my research projects, I have used “radiomics” analysis to extract and apply textural features of brain and neck tumors for diagnosis and prognosis prediction. “Radiomics” refers to computerized assessment of medical images for extraction of features that are often hidden from human eyes. With this technique, we can extract thousands of features and data points from CT, MRI, or PET studies and apply these information to devise quantitative and objective prognostic biomarkers, which can improve outcome prediction, and treatment planning in cancer patients. Machine-learning algorithms can combine such radiomics features with anatomical findings and clinical data to develop survival models for prognostic classification of patients with brain or neck cancer. Such accurate risk stratification and prognostic staging are the first steps toward achieving “personalized” treatment decisions and “precision medicine” practice in this field.
The University of California, Davis Medical Center is fortunate to have two innovative technological advances this year. We are one of the first academic radiology departments in North America to have access to an ultra-high resolution research-clinical CT scanner (Precision Aquilon). This CT system has at least 16x the spatial resolution of a conventional CT scanner giving it the ability to resolve anatomic structures to a level never seen before. Additionally, we also have access to the world’s first high-resolution whole-body PET-CT system (EXPLORER). These imaging technologies will help advance my interest in using artificial intelligence, which utilizes computers and machine learning to improve the diagnosis, prognosis, and treatment of neurological diseases with the ultimate goal of improving patient care outcomes.
My lab develops computational imaging solutions for improved diagnosis and treatment of diseases. One research direction aims to develop methods for reducing radiation dose and costs for imaging procedures. For example, a widely used modality for diagnosing coronary artery disease, the leading cause of death in the USA, is single photon emission computed tomography (SPECT) imaging. Typically, a CT scan is acquired with the SPECT scan to compensate for clinically significant distortions in SPECT images. However, acquiring the CT image leads to increased radiation dose and costs, and potentially inaccurate diagnosis if the patient moved between the SPECT and CT scans. To address these issues, we are developing methods that could compensate for the distortions without using the CT scan. For this project, we recently received the NIBIB Trailblazer award for early-stage investigators.In another research direction, we are developing computational methods to obtain biomarkers from medical images with the goal of personalizing medicine in patients with cancer. In one project, we observed in a prospective clinical trial that a set of machine-learning-based methods we had developed to quantify metrics from diffusion MR images were important for predicting therapy response at an early stage in patients with liver metastasis. This is highly significant since early prediction can help adapt the therapy regimens for the patients, thus helping to reduce side-effects and therapy costs, and potentially yield improved clinical outcomes. Similarly, we recently developed artificial-intelligence (AI)-based methods to quantify metrics from positron emission tomography (PET) images of patients with lung cancer, again with the goal of predicting therapy response at an early stage and thus adapting therapy. Research along this direction will be the focus of my presentation at the Medical Imaging Technology Showcase.
My research interests focus on development and application of advanced MRI technology for: 1) improving diagnosis of musculoskeletal disease and 2) improving in vivo characterization musculoskeletal tissue function in health and disease. Current therapeutic advances to treat cartilage degeneration are limited by a lack of noninvasive imaging markers of cartilage health. We are developing and validating quantitative MRI approaches for detecting early degenerative changes within cartilage tissue to better monitor disease progression and therapeutic intervention. An additional area of work in my lab is the development and application of microvascular and metabolic function in skeletal muscle. Skeletal muscle is implicated in the progression and outcome a variety of diseases such as diabetes, kidney disease, and heart disease. We are actively developing and utilizing several noninvasive MRI tools to better understand and characterize physiological changes with different disease states to aid the development of therapeutic strategies.
My research interest includes development and optimization of advanced imaging modalities for women’s and fetal health applications.Potential topics include:
– What the current standard of care for female health evaluation is,
– Limitations of the current standard of care,
– Physiological changes of the female body that prevent access to the standard of care at different stages in life,
– Information lacking in the literature needed for the improvement of women and fetal care,
– The role of quantitative MRI in improving women’s healthcare.
– MRI screening and staging of female pelvic cancers and breast cancer
My main research interests are: To examine the use of different novel radiotracers (medication/chemical structure connected to a radioactive element) and imaging devices to detect different cancers. Each cancer may be unique in its behavior. Different tracers have different mechanism of actions And understanding each tracer’s properties and distribution in different type of cancers may add to the ability to localize regions of cancer involvement in the body and potentially may lead to a better treatment methodology.In the past, as part of a research team I investigated the use of amino acid analog positron emission tomography (PET) radiotracer fluciclovine (FACBC, Axumin) in different cancers, such as prostate, breast, and lung cancers. These investigations led to the Food and Drug Administration (FDA) approval of fluciclovine for prostate cancer imaging.
Currently, as a PI, I received a small grant to investigate the feasibility of FACBC PET radiotracer in the use of gynecological cancers, such as ovarian, endometrial, and cervical cancers. Preliminary data are promising. My goal is to develop prospective protocols for each of the above-mentioned cancers to investigate the ability of fluciclovine PET tracer to localize areas of malignancy. The rationale is that up until recently F18-Fluorodeoxyglucose (FDG) tracer was the only FDA approved tracer for PET imaging. FDG enters the cell via glucose transporters (GLUT). GLUT are highly expressed in cancer and inflammatory cells which may decrease its ability to localize deferent cancers. FDG normally washes out from the body by the kidneys which results in an intense urine accumulation. This may obscure areas of cancer involvement in the pelvis. Therefore, there is a need for a better biomarker for accurate imaging. Fluciclovine is a synthetic amino acid PET tracer, proven to successfully localize small prostate cancer lesions. As well, fluciclovine uptake is not very high in inflammatory cells and has a very slow renal washout, resulting in a clear view of the pelvis. Fluciclovine is not a prostate cancer cell-specific tracer and will enter the cell by the amino acid transporters. Amino acid transporters are overexpressed in ovarian, endometrial and cervical cancer cells. Thus, we believe that fluciclovine will enter these cells in higher concentration compared with benign cells.
The lab mission is to develop molecular and cellular solutions addressing improtant challenges in biomedical science and clinical medicine. We create small molecules, engineered proteins and cell-based tools that can “light up” and control in vivo biology using principles of chemical and synthetic biology. When possible, our tecnologies are translated to the clinic using nuclear medicine and molecular imaging techniques. For example, our group recently pioneered the development, preclinical testing, and human application of a new class of positron emission tomography (PET) radiotracers based on the small molecule antibiotic trimethoprim. Thesemolecules have diverse applications in our broad fields of investigation including cancer biology, immunology, and infectious disease.
My broad research interests include non-invasive imaging to personalize cancer care through improvements in detection, monitoring and therapy of cancer in both preclinical and clinical cancer research. My research goals include prediction of early response to breast cancer treatment and extracting numerical quantitative information from medical images obtained from clinically-relevant imaging modalities (US, MRI, and PET) to inform individualized treatment decisions for cancer therapy. From medical imaging, we can identify variations in the underlying biology of each unique tumor which provides a potential avenue to improve and tailor drug delivery of current treatments, such as chemotherapy, radiation therapy and immunotherapy. My focus of ongoing research will be using FDA-approved imaging devices to enhance delivery of current standard-of-care therapies inbreast cancer, thereby creaitng a quick route for clinical translation. This research has potential to optimize standard-of-care therapies by replacing them with patient-specific regimens that are more effective and less toxic.
SCOPE OF PROBLEM: Atherosclerosis is a leading cause of death in the world, causing ~15 million deaths every year. While the number and size of atherosclerotic plaques increase by age, most plaques do not cause symptoms throughout the life of an individual. However, occasionally, a plaque may rupture, which may cause clotting and sudden occlusion of a vessel, leading to the most dreaded complications of atherosclerosis, for example myocardial infarction, stroke, and death. However, currently, there is no reliable clinical test to accurately identify specific plaques that are at risk of complication, among many others that are not.STRATEGY & GOAL: The goal of our research is to validate novel imaging techniques for detection of plaques that are risk of complication through detection of underlying molecular and cellular events leading to plaque rupture. We are specifically focused on detection of inflammation within the vessel wall, as a key factor in pathogenesis of atherosclerosis and its complications.
Coming Soon.
I am a Research Track Diagnostic Radiology resident at Emory University. My scientific focus is on investigating the role of advanced diagnostic techniques in the assessment of microstructural and functional brain changes not readily assessed with conventional imaging modalities. My prior research has encompassed radiologic-pathologic correlations in various brain pathology and assessment of imaging patterns of CNS involvement in clinically asymptomatic HIV patients. More recently, my work has centered on investigation of functional and structural brain connectivity alterations in patients post traumatic brain injury. I am also interested in comparative effectiveness, appropriateness and utilization of diagnostic imaging tests in neuroradiology, machine learning and deep learning algorithms and 3D printing in neuroradiology.The topic of my presentation at the Medical Imaging Technology Showcase will be functional brain connectivity alterations in patients experiencing persistent vestibular symptoms (dizziness and vertigo) after concussion using task-based and resting state functional MRI.
The goal of my research is to use medical imaging to develop more effective strategies for the treatment of cancer, specifically to use imaging to customize treatment to individual patient needs. Personalized treatments may result in fewer side effects and improved patient outcomes compared to standard options. My research focuses onteh use of MRI a form of radiological imaging that uses powerful magnets, radio waves, and a computer to take detailed pictures inside the human body. My research involves developing new MRI tecniques to improve how well we can image cancer. Goals of this research are to use MRI to better determine how aggressive cancers are before they are treated, to better monitor the effetiveness of treatments and, if and when cancers come back after treatment, to be able to detect these recurrences at the earliest possible time point so that additional personalized treatment can be provided to patients.