Bio


Dr. Shokrollahi is a research scholar at the Department of Radiology, with a research focus on developing an artificial intelligence system for preimage protocols on magnetic resonance imaging (MRI) and computerized tomography (CT) scans. He completed his PhD at the University of Toronto (UofT) in Biomedical Engineering before moving to the Radiology Department in 2020. He earned his master's and a bachelor's degree in Electrical and Computer Engineering. His research focused on developing magnetically actuated devices, MRI-compatible surgical robots, and machine learning techniques for medical applications. He developed and implemented a magnetically actuated microrobot for sampling microbiome in the gastrointestinal tract, an MRI-compatible robot for pediatric bone biopsy, and an automated system for detecting eye movements during sleep. In addition to research, Dr. Shokrollahi taught four courses at the undergraduate and graduate level at UofT and Ryerson University, Toronto, Canada. He is a member of the International Society for Magnetic Resonance in Medicine.

Stanford Advisors


Patents


  • Peyman Shokrollahi, James M. Drake, Andrew A. Goldenberg. "Magnetic Resonance Imaging (MRI) Compatible Force Sensor"
  • Peyman Shokrollahi, Eric Diller, and John Parkinson. "Magnetically Actuated Capsule for Sampling Microbiome in the Gastrointestinal Tract"

All Publications


  • A study on observed ultrasonic motor-induced magnetic resonance imaging (MRI) artifacts Elsevier - Biomedical Journal Shokrollahi, P., et al 2019
  • Signal-to-noise ratio evaluation of magnetic resonance images in the presence of an ultrasonic motor Biomedical Engineering Shokrollahi, P., et al 2017
  • Ultrasonic motor-induced geometric distortions in magnetic resonance images Medical & Biological Engineering & Computing Shokrollahi, P., et al 2017
  • Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System Multidisciplinary Digital Publishing Institute - Actuators Shokrollahi, P., et al 2017
  • Quantification of Force and Torque Applied by a High-Field Magnetic Resonance Imaging System on an Ultrasonic Motor for MRI-Guided Robot-Assisted Interventions Medical & Biological Engineering & Computing - Actuators Shokrollahi, P., et al 2017