Jamie Schroeder, MD, PhD

Title(s)Assistant Professor, Radiology
SchoolSchool of Medicine
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    Collapse Biography 
    Collapse Education and Training
    UCSF06/2020Cardiac and Pulmonary Imaging Fellowship
    Johns Hopkins Hospital06/2019Diagnostic Radiology Residency
    Johns Hopkins School of Medicine, Baltimore, MDMD06/2014NIH-MSTP
    University of Oxford, Oxford, UKDPhil08/2010Department of Physiology, Anatomy, and Genetics
    Stanford University, Stanford, CABS/MS06/2005Biological Sciences / Bioengineering

    Collapse Overview 
    Collapse Overview
    Jamie Lee Twist Schroeder MD DPhil joined the UCSF Radiology and Biomedical Imaging Cardiac & Pulmonary section in March 2022.

    Prior to joining the faculty, Dr. Schroeder completed a master’s degree in Bioengineering at Stanford University. She earned a DPhil at the University of Oxford from the Department of Physiology, Anatomy, and Genetics as part of the NIH-Oxford-Cambridge Scholars Medical Scientist Training Program. Dr. Schroeder obtained her medical degree from Johns Hopkins School of Medicine and completed an internal medicine internship at Sinai Hospital of Baltimore. Her four-year diagnostic radiology residency was completed at Johns Hopkins Hospital, followed by a one-year fellowship in Cardiac and Pulmonary Imaging at UCSF. Prior to joining the UCSF Radiology faculty, Dr. Schroeder was a staff physician in the Cardiovascular/Thoracic Imaging section of the Stanford-affiliated Palo Alto Veterans Affairs Medical Center.

    Dr. Schroeder was the sole recipient of the 2021 North American Society of Cardiac Imaging (NASCI) Rising Star Fellowship Award and is a past recipient of the RSNA’s Student Travel Award and the American Association for Thoracic Surgery’s Summer Research Scholarship. She has authored peer-reviewed publications on various topics including clinical radiology, cardiovascular disease, and novel imaging technologies. Dr. Schroeder’s recent projects focus on improving clinical decision-making in cardiac imaging and include the development of an unsupervised machine learning pathway to analyze motion-corrected time-course data from myocardial perfusion MRI with the goal of systematizing the diagnostic process.