S20-375 Predicting Future Images and Imaging Biomarkers with Deep Learning Researchers at Stanford University present a method to predict biomarkers that are correlated with poor disease prognosis from imaging data. Greg Zaharchuk Fabian Reith
S19-153 Virtual Control Arms for Clinical Trials using Deep Learning Researchers at Stanford University have developed a deep learning software algorithm that allows physicians running clinical trials to predict control patient outcomes using virtual control arms. Greg Zaharchuk
S17-392 Improved MRI reconstruction using deep learning, generative adversarial network and acquisition signal model Stanford researchers have developed novel methods to achieve more efficient, accurate and generalizable reconstruction from under-sampled MRI. Enhao Gong Greg Zaharchuk John Pauly Morteza Mardani
S17-364 MRI Acquisition Trajectory Optimization based on prior knowledge of image content, sensitivity and k-space statistics Researchers at Stanford developed a method to improve both the efficiency and the performance of MRI optimal trajectory design. Enhao Gong John Pauly Greg Zaharchuk Suchandrima Banerjee