School of Medicine
Showing 1-6 of 6 Results
Bhavik Natvar Patel
Assistant Professor of Radiology (Body Imaging) at the Stanford University Medical Center
Current Research and Scholarly Interests Advanced CT, MRI, & Ultrasound Techniques & Applications
Artificial Intelligence (Machine Learning & Deep Learning)
John M. Pauly
Reid Weaver Dennis Professor
Bio Interests include medical imaging generally, and magnetic resonance imaging (MRI) in particular. Current efforts are focused on medical applications of MRI where real-time interactive imaging is important. Two examples are cardiac imaging, and the interactive guidance of interventional procedures. Specific interests include rapid methods for the excitation and acquisition of the MR signal, and the reconstruction of images from the data acquired using these approaches.
Kim Butts Pauly
Professor of Radiology (Radiological Sciences Lab) and, by courtesy, of Electrical Engineering and of Bioengineering
Current Research and Scholarly Interests We are investigating and developing, and applying focused ultrasound in neuromodulation, blood brain barrier opening, and ablation for both neuro and body applications.
Boston Scientific Applied Biomedical Engineering Professor and Professor of Radiology, Emeritus
Current Research and Scholarly Interests Broadly, Dr. Pelc is interested in the physics, engineering and mathematics of medical imaging, especially computed tomography, digital x-ray imaging, magnetic resonance imaging, and hybrid multimodality systems. His current research is concentrated in the development of computed tomography systems with higher image quality and dose efficiency, in the characterization of system performance, and in the development and validation of new clinical applications.
Sylvia K. Plevritis, PhD
Professor of Biomedical Data Science and of Radiology (Integrative Biomedical Imaging Informatics at Stanford)
Current Research and Scholarly Interests My research program focuses on computational modeling of cancer biology and cancer outcomes. My laboratory develops stochastic models of the natural history of cancer based on clinical research data. We estimate population-level outcomes under differing screening and treatment interventions. We also analyze genomic and proteomic cancer data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.