What is the impact of AI and deep learning on clinical workflows? Enhao Gong and Greg Zaharchuk offer an overview of AI and deep learning technologies invented at Stanford and applied in the clinical neuroimaging workflow at Stanford Hospital, where they have provided faster, safer, cheaper, and smarter medical imaging and treatment decision making.
Specific use cases include:
Enhao Gong is a PhD student in electrical engineering at Stanford, where he is advised by John Pauly (electrical engineering) and Greg Zaharchuk (radiology). As founder and researcher at Subtle Medical, he is pushing the performance of deep learning methods to boost the efficiency and value for medical imaging. His research focuses on applying machine learning, deep learning, and optimization for medical imaging reconstruction and processing. Recently, Enhao has been working to bridge deep learning methods with MRI reconstruction, such as enhancing image quality with deep learning and multicontrast information, solving quantitative imaging (water-fat separation, QSM, parameter mapping) using deep learning frameworks, and using generative adversarial networks (GANs) for compressed sensing MRI.
Greg Zaharchuk is an associate professor in radiology at Stanford University and a neuroradiologist at Stanford Hospital. His research interests include deep learning applications in neuroimaging, imaging of cerebral hemodynamics with MRI and CT, noninvasive oxygenation measurement with MRI, clinical imaging of cerebrovascular disease, imaging of cervical artery dissection, MR/PET in neuroradiology, and resting-state fMRI for perfusion imaging and stroke.
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