Stanford University
Showing 21-30 of 299 Results
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Robert Downey Boutin
Clinical Professor, Radiology
BioDr. Boutin works clinically as a musculoskeletal radiologist interpreting a broad array of diagnostic imaging examinations, most frequently MRI, CT, sonography, and radiography.
The clinical focus for Dr. Boutin is MRI and CT of joints, bones, and muscles, as well as adding value to routine radiology exams by translating advancements from fields of artificial intelligence and imaging informatics. These advancements include enhancing routine imaging exams to help promote physical function, quality of life, and healthspan in our patients. Because the worldwide population of people > 60 years of age is projected to double by 2050, there is a crucial need for improvements in the accurate and efficient management of derangements associated with biological aging, including osteoporosis, sarcopenia, and adiposity.
Dr. Boutin was introduced to orthopaedics at an early age by his father and older brother -- both orthopaedists. After earning undergraduate and graduate degrees at Stanford University, Dr. Boutin completed advanced fellowship training in Musculoskeletal Imaging at the University of California, San Diego, and worked on the faculty at Harvard Medical School. Core professional values include a commitment to outstanding quality, compassion, safety, and integrity.
Dr. Boutin is a leader in musculoskeletal imaging. He is the President of the Society of Academic Bone Radiologists and serves on the Appropriateness Criteria Panel for the American College of Radiology. He is an Assistant Editor at the American Journal of Roentgenology (AJR), and recently served as the Chair of the Musculoskeletal Imaging Program Committee for the largest medical meeting in the world (RSNA).
He is a frequent invited lecturer for continuing medical education courses, such as those at the annual meetings of the Radiological Society of North America, the International Skeletal Society, and the American Roentgen Ray Society. He also lectures regularly at the international Orthopaedic Imaging course in Davos, Switzerland. -
Sujatha Buddhe
Clinical Professor, Pediatrics - Cardiology
Clinical Associate Professor (By courtesy), Radiology - Pediatric RadiologyBioDr. Buddhe earned her medical degree from the Osmania Medical College, Hyderabad, India. She completed her pediatrics residency and chief residency at the Brookdale University Hospital and Medical Center, NY and pediatric cardiology fellowship at the Children's Hospital of Michigan. Her advanced fellowship training in pediatric cardiac non-invasive imaging was completed at the Columbia University College of Physicians and Surgeons, Morgan Stanley Children's Hospital in New York. She earned her Masters degree in Clinical research and statistical analysis at the University of Michigan, Ann Arbor. She worked at Seattle Children's Hospital, University of Washington for almost ten years where she served as the Director of Non-invasive Imaging research and the Co-Director of cardiac MRI program. Her research interests include echocardiography and Cardiac MRI.
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Stephanie Tzu-Ying Chang
Assistant Professor of Radiology
BioDr. Stephanie Chang is an Assistant Professor in Radiology at Stanford University School of Medicine, Section Chief in Abdominal Imaging, and Interim Director of Diagnostic Radiology in the Department of Radiology at VA Palo Alto.
Dr. Chang’s research program aims to utilize state-of-the-art diagnostic technology to non-invasively detect abdominal diseases that predispose or lead to cancer since the most beneficial and cost-effective cancer imaging occurs in the screening/early detection phase.
Currently, metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) are leading worldwide causes of end-stage liver disease and liver cancer (hepatocellular carcinoma, HCC). To determine the optimal multimodality screening and diagnostic pathway, Dr. Chang leads prospective research studies at VA Palo Alto comparing blood-based and imaging-based diagnostic tests – the Fibrosis-4 (FIB-4) Index, the Enhanced Liver Fibrosis (ELF) test, vibration controlled transient elastography (VCTE), ultrasound point shear wave elastography (US pSWE), and magnetic resonance imaging (MRI) elastography – among high-risk veterans with obesity and/or type 2 diabetes to compare relative performance and costs/benefits.
To improve detection of hepatocellular carcinoma (HCC) among patients with end-stage liver disease (cirrhosis), Dr. Chang also serves as a member of the VA National Radiology Working Group and radiology local site investigator at VA Palo Alto for a multicenter VA research study CSP #2023 PREventing liver cancer Mortality through Imaging with Ultrasound vs. MRI (PREMIUM STUDY) comparing US and MRI to determine optimal screening strategies for hepatocellular carcinoma.
While MRI has the overall highest sensitivity and specificity for many diseases and contains additional biological/prognostic information not obtainable via other imaging methods, the utility of MRI as a powerful non-invasive screening tool has been hampered by its limitations in access, time, and cost. Dr. Chang is interested in significantly shortening MRI exam times to accelerate MRI throughput, decrease patient discomfort, and reduce costs. To accomplish this goal, Dr. Chang is interested in improving pulse sequences to shorten MRI acquisition times while maintaining or increasing diagnostic quality. In addition, Dr. Chang is interested in novel MRI techniques to improve disease detection and staging. -
Akshay Chaudhari
Associate Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and of Biomedical Data Science
Current Research and Scholarly InterestsDr. Chaudhari is interested in the application of artificial intelligence techniques to all aspects of medical imaging, including automated schedule and reading prioritization, image reconstruction, quantitative analysis, and prediction of patient outcomes. His interests focus on the development and evaluation new self-supervised and representation learning techniques for multi-modal deep learning in healthcare using vision, language, and medical records data
