School of Medicine


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  • Safwan Halabi

    Safwan Halabi

    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.

  • Carl Herickhoff

    Carl Herickhoff

    Research Engineer, Rad/Pediatric Radiology

    Bio Dr. Herickhoff has worked in ultrasound R&D in academia and industry since 2005. His research interests include IVUS transducer design and imaging methods, "Smart" 3D ultrasound (image acquisition methods, reconstruction techniques, improved display, and automated diagnosis), and capacitive micromachined ultrasonic transducers (cMUTs).

  • Dongwoon Hyun

    Dongwoon Hyun

    Research Engineer, Rad/Pediatric Radiology

    Bio My research interests are focused on developing and implementing novel beamforming techniques to improve the quality and diagnostic value of ultrasound images. Current projects include improving image quality in difficult-to-image patients, enhancing the sensitivity of molecular contrast-enhanced ultrasound imaging, reducing common artifacts in ultrasound imaging using machine learning-based methods, and the rapid translation of these techniques onto real-time ultrasound imaging systems using GPU-based computing.

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