School of Medicine
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Safwan S. Halabi, MD
Clinical Associate Professor, Radiology - Pediatric Radiology
Bio Dr. Safwan Halabi is a Clinical Associate Professor of Radiology at the Stanford University School of Medicine and serves as the Medical Director for Radiology Informatics at Stanford Children's Health. He is board-certified in Radiology with Certificate of Added Qualification in Pediatric Radiology. He is also board-certified in Clinical Informatics. He clinically practices obstetric and pediatric imaging at Lucile Packard Children's Hospital. Dr. Halabi’s clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. He has also lead strategic efforts to improve the enterprise imaging platforms at Stanford Children’s Health. He is a strong advocate of patient-centric care and has helped guide policies for radiology report and image release to patients. He has published in peer-reviewed journals on various clinical and informatics topics. His current academic and research interests include: imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support and patient-centric health care delivery. He is currently the Chair of the RSNA Informatics Data Science Committee and serves as a Board Member for the Society for Imaging Informatics in Medicine.
Affiliate, Rad/Pediatric Radiology
Bio Dr. Herickhoff has broad experience in ultrasound research & development, spanning 14 years in both academia and industry (GE Global Research and Philips Medical Systems). He currently serves as Deputy Director of the Radiological Sciences Laboratory, and his current research interests include novel medical ultrasound transducer designs and methods for elasticity, contrast, and flow imaging, and specialized ultrasound imaging systems for pediatrics and interventional procedure guidance.
Research Engineer, Rad/Pediatric Radiology
Bio My research interests are focused on the development and clinical translation of new ultrasound imaging techniques to improve the quality and diagnostic value of ultrasound imaging. My interests are in clinical translation of ultrasound molecular imaging for early cancer detection, improving image quality in difficult-to-image patients, and to reduce noise artifacts in ultrasound images. In my research, I have refined adaptive beamforming methods such as coherence-based imaging, helped to pioneer the use of deep learning tools on raw ultrasound data to produce more accurate B-mode images and more sensitive ultrasound molecular images, and developed GPU-based software beamforming tools to deploy these methods in real-time on experimental and clinical imaging systems.