Jeannine Ruby, MD, recently departed Abdominal Section fellow in the Department of Radiology, received the American Roentgen Ray Society’s Magna Cum Laude award for the “best in subspecialty” oral presentation at the ARRS 2019 Annual Meeting and Scientific Program. This award was based on her presentation and outstanding research paper in Gastrointestinal imaging, with the abstract titled “Traditional Serrated Adenomas at CT Colonography: International Multicenter Experience.”
Department researcher Peter Graffy won a Cum Laude award at the same ARRS conference, for his presentation “Opportunistic Osteoporosis Screening at Routine Body CT: Normative L1 Trabecular Attenuation Values From Over 20,000 Data Points.”
Scientists and researchers around the world are reaching an entirely new level of discovery with Artificial Intelligence (AI), using data to solve problems and enabling decisions in innovative new ways. AI remains a dominant conversation at scientific symposia across the globe and once again received top billing at RSNA and ISMRM annual meetings. One need not travel far to see how AI is creating new frontiers – it is also a big part of the research being conducted at the University of Wisconsin Department of Radiology.
The term AI dates back to the 1950s, but its present state is possible through the development of supercomputers. Advances in computer hardware over the past decade have paved the way for very complex algorithms to run quickly and easily, producing data faster than we could ever have imagined before. The Department has made the commitment to create a robust technical infrastructure to support innovation and encourage the expansion of AI in Radiology here at UW. Our Director of Informatics, John Garrett, PhD, is currently focusing on developing the infrastructure to support faculty and provide access to the type of equipment needed to process the large amounts of data necessary for Deep Learning (DL), a subset of AI.
The department invested in a GPU supercomputer, a networked group of computers whose processing power far surpasses the capabilities of even the most powerful CPU. “These are the same computers used in gaming applications,” Garrett said. “The power of the GPU processing is equivalent to a whole server farm – this is the type of computer we want to get people used to using to be able to do things they simply can’t on other computers,” he said.
Dr. Garrett noted that there are challenges to incorporating AI into a traditional hospital network, and to be able to integrate it into the clinical workflow. That is where many of the department faculty are closing the gap.
Vivek Prabhakaran, MD, PhD, is Department of Radiology faculty member who is using AI to make new discoveries. He has led the efforts to train an AI model using MRI input to create synthetic FDG PET images. Traditional FDG PET scans are currently the standard for understanding brain metabolism and diagnosing Alzheimer’s disease. They involve the injection of a radioactive tracer, fluorodeoxyglucose, or FDG, into the patient before the PET scan, to show the differences between the healthy and diseased tissue in the brain. Dr. Prabhakaran’s synthetic version using MRI involves no radiation and had a 97 percent correlation rate with the test that used radiation. “Not only is this new method non-invasive, without the injection of radioactive tracers, but it can be done at a much lower cost,” Dr. Prabhakaran said. The AI algorithm used by Prabhakaran has great potential to be expanded to other uses, for example to help diagnose cancer, epilepsy and myocardial viability.
Alan McMillan, PhD, director of the Molecular Imaging / Magnetic Resonance Technology Lab (MIMRTL) is one of “superusers” of the NVIDIA® DGX™ supercomputer system, seeking ways to advance MRI, PET/MR and PET/CT imaging techniques. “There is no aspect of our research that doesn’t incorporate AI,” McMillan said. In fact, he sees the rapid growth of AI as a boon to all future research, allowing machines to focus on the algorithms and enabling people to focus on the more physical aspects of human health. Some of McMillan’s recent efforts have centered on finding a way to create CT-like imaging from MRI images. Using Deep Learning, Dr. McMillan’s team has been able to reconstruct robust CT images directly from clinical MR images. When these images are used to perform attenuation correction, they enable more quantitatively accurate PET images for simultaneous PET/MR and reduce error from 10-25% (in conventional techniques) to less than 5%.
Another researcher and clinician who is tapping into AI to discover new information is Perry Pickhardt, MD. Dr. Pickhardt recently found a new way to obtain additional “opportunistic” diagnostic information about the structures seen in the abdomen during abdominal Computed Tomography (CT) imagery performed during CT colonography. “CT sees many things,” Dr. Pickhardt said. “We wanted to take the extra biomarkers in the CT scans and try to do something more with them. A CT scan also has information about patients’ bone, muscle, fat, calcium, and more,” Pickhardt continued.“With this, we can get a read on many other important health connotations, for example, we can quantify the aortic calcium levels of a patient and make a prediction about his or her likelihood of a cardiac event.” Dr. Pickhardt’s paper, “Fully-Automated Analysis of Abdominal CT Scans for Opportunistic Prediction of Cardiometabolic Events: Initial Results in a Large Asymptomatic Adult Cohort,” received the Roscoe E. Miller Best Paper Award at the recent Society of Abdominal Radiology (SAR) conference.
Vivek Prabhakaran, MD, PhD, is another Department of Radiology faculty member who is using AI to make new discoveries. He has led the efforts to train an AI model using MRI input to create synthetic FDG PET images. Traditional FDG PET scans are currently the standard for understanding brain metabolism and diagnosing Alzheimer’s disease. They involve the injection of a radioactive tracer, fluorodeoxyglucose, or FDG, into the patient before the PET scan, to show the differences between the healthy and diseased tissue in the brain. Dr. Prabhakaran’s synthetic version using MRI involves no radiation and had a 97 percent correlation rate with the test that used radiation. “Not only is this new method non-invasive, without the injection of radioactive tracers, but it can be done at a much lower cost,” Dr. Prabhakaran said. The AI algorithm used by Prabhakaran has great potential to be expanded to other uses, for example to help diagnose cancer, epilepsy and myocardial viability.
Innovations made possible by AI are occurring in practically every area of the imaging world. Department of Radiology researchers Richard Kijowski, MD, and Fang Liu, PhD, have developed a fully automated deep-learning system that uses two deep convolutional neural networks to detect anterior cruciate ligament ears (ACL) tears on knee MRI exams.The result of their research showed that a fully automated deep learning network could determine the presence or absence of ACL tears with similar diagnostic performance as experienced musculoskeletal radiologists.Similar deep learning algorithm have been developed by Drs. Kijowski and Liu to detect other musculoskeletal pathology, including cartilage lesions on MRI and hip fractures on pelvic radiographs. The use of deep learning methods to detect musculoskeletal pathology could provide immediate preliminary interpretations of imaging studies, maximize diagnostic performance, and reduce errors due to distraction and fatigue. “However, future work is needed for further technical development and validation before this could be implemented into clinical practice,” said Dr. Kijowski. “I look forward to the future of this research.”
The evolution of AI is currently thriving across UW campus, and is especially evident in the Department of Radiology. But many are wondering, what should we expect from the future of AI? “My hope for AI is that it will create a better use for people’s intelligence,” Dr. McMillan said. “We can decide what problems are most important, and use AI to solve problems in faster and better ways. For many scientists, AI need not be the focus of their research, rather it can be leveraged as a tool that enables us to solve problems that couldn’t easily be solved in any other way,” he said. “The hope for the future is that we will keep finding more hard problems for AI to solve more quickly and efficiently. It is truly humbling and exciting work.”
UWSMPH Department of Radiology faculty Dr. Richard Kijowski, in the Musculoskeletal Imaging and Intervention Section, was invited to serve as a member of the Skeletal Biology Structure and Regeneration Study Section. Center for Scientific Review, for the National Institutes of Health. Dr. Kijowski will serve a four-year term in this role, participating in the assurance of quality of the NIH peer review process. Congratulations, Dr. Kijowski!
Department of Radiology MRI/MBA Fellow Liisa Bergmann, MD, was recently selected to receive an ACGME Back to Bedside grant. This initiative is intended to directly support residents and fellows as they lead projects that increase patient engagement and shape clinical learning environments in a meaningful manner.
Dr. Bergmann’s work focuses on patient access to radiologists after cardiac imaging exams. Historically, radiology results have been communicated to patients via the referring doctor who ordered the imaging. Less than one year ago, UW Health patients were provided with their full radiology reports online via the electronic medical record, without the counsel of any physician. Radiologists providing “direct-to-patient reporting” goes beyond this to help alleviate patient anxiety from having access to a very technical, scientific report that is written for other physicians. Patients who have undergone cardiac imaging have the option to meet with the radiologist personally, to enhance their comprehension of the information by walking through the report and images of their own anatomy.
Dr. Bergmann is excited to be recognized by the ACGME for her project. “There is a trend toward increased patient interaction across all of radiology,” Dr. Bergmann said. “I hope this project will be an exciting form of health science education for curious patients.”
Jeffrey Kanne, MD, Professor of Radiology and Chief of Thoracic Imaging, has co-authored two manuscripts related to vaping-induced lung disease published in the New England Journal of Medicine. This subject has gained national attention in the past months, with a spike in related health incidents related to the practice of vaping, especially in adolescents.
Vaping has increased among teenagers, particularly high school students, in 2018. The FDA reports that over 3.5 million teens reported using e-cigarettes in 2018, which has increased from 11.7% to 20.8% since 2017. While e-cigarettes are still fairly new, the long-term effects are not fully understood. This research is crucial because it can reveal the short-term risks of this growing cultural trend.
Vaping-induced lung injury is an emerging public health threat, however the exact causes are unknown. Dr. Kanne’s published work reveals some of the patterns associated with vaping injuries. It represents the first step to identify more similar patterns to determine the root causes of pathophysiology due to vaping. The full articles can be viewed at:
Imaging of Vaping-Associated Lung DiseasePulmonary Illness Related to E-Cigarette Use in Illinois and Wisconsin — Preliminary Report.
Thomas Grist, MD, the John H. Juhl Professor of Radiology and Medical Physics and Chair of the Department of Radiology at the University of Wisconsin–Madison, submitted an editorial published in this month's issue of Radiology, published by the Radiological Society of North America. See more @UWiscRadiology and @radiology_rsna on Twitter, or read full article here.
Victoria Rendell, MD, General Surgery Resident at the UW Department of Surgery, recently received the esteemed Lauterbur Award for her presentation on her research at the Society of Advanced Body Imaging (formerly the Society of Computed Body Tomography & Magnetic Resonance). The Lauterbur award is given for the best MR oral scientific presentation each year. Dr. Rendell was recognized for her research on “Direct Radiologic-Pathologic Correlation of Liver Lesions with an MR-Compatible Localization Device."
Dr. Rendell’s research project was a collaboration between her department and the UWSMPH Department of Radiology. This project was completed with the collaboration of Emily Winslow, MD, FACS, then a general surgeon in the UW Department of Surgery, and Scott Reeder, MD, Professor in the UW Department of Radiology. “Dr. Rendell did a masterful job of presenting her project at the SABI Conference,” said Dr. Reeder. “In my view, this is a fabulous example of collaboration between Surgery and Radiology. This work was noticed by many in the society and has helped advance UW’s national reputation,” Dr. Reeder said. “I am confident this is just the start of great things to come for Dr. Rendell!”
The Lauterbur award is named after Paul C. Lauterbur, a faculty member of the University of Illinois, who was awarded the Nobel Prize in Physiology or Medicine in 2003. Dr. Lauterbur is credited with inventing the MRI. The award is highly prestigious in abdominal imaging circles, and is considered by many in the radiology field to be the pinnacle of scientific awards. There were only 16 abstracts that were selected out of 160 total presentations to present in the scientific session. Out of the 16 who presented, Dr. Rendell’s presentation was considered the best. Congratulations to Dr. Rendell on this significant accomplishment!
Dr. Diego Hernando, PhD, Assistant Professor and Director of Quantitative Imaging in the UW School of Medicine and Public Health Department of Radiology, was recently awarded the Response to Therapy Session Award at the Society for Advanced Body Imaging 2019. Dr. Hernando won this award for his presentation of his research on “Repeatability and Reproducitibility and Confounder-Corrected R2* as a Biomarker of Liver Iron Concentration: Interim Results from a Multi-Center, Multi-Vendor Study at 1.5T and 3T.”
This work demonstrates that the methods for MRI-based iron quantification, which aim to assess the overload of the liver iron, work reliably and reproducibly across various MRI scanners at different centers. This is important for the broad dissemination of MRI-based iron quantification, which is necessary for the diagnosis, staging, and treatment monitoring of iron overload. Congratulations to Dr. Hernando for his outstanding work!
Thomas M. Grist, MD, FACR, Chair of the UWSMPH Department of Radiology, delivered the Report of the RSNA Research and Education Foundation at the kickoff Plenary Session of the 2019 Annual Meeting of the Radiological Society of North America (RSNA).
In his presentation, Dr. Grist discussed improvements in the imaging field including artificial intelligence, new imaging methods, and novel applications of current imaging techniques. He reported that the Foundation approved over $5 million in Radiology Research and Education grants this year, a record amount. In 2019 alone, the Foundation will support over 100 recipients from 48 different institutions. This year there were 17 grants funded in the area of Artificial Intelligence and machine learning, these grants being made possible through donations to the Foundation. See Dr. Grist's full presentation here.
On October 22, 2019, Gina Greenwood and Dr. Maria Daniela Martin participated as panelists in a UWSMPH hosted seminar for the UW Women in Science and Engineering (WISE) learning community (https://www.housing.wisc.edu/residence-halls/learning-communities/wise/). The seminar was organized by Dr. Elizabeth Sadowski and Suzanne Swift (WISE), and sponsored by Dean Golden. A group of accomplished UWSMPH women faculty and staff volunteered their time and shared their career experiences and wisdom with over 125 UW undergraduate women who are just beginning their career journeys. From physician to physicist, the students heard how STEM majors can pursue a variety of careers in medicine and health care. The students left with more knowledge and confidence to pursue careers in healthcare, knowing there are women who have taken these paths and can help them along the way.