PhD Proposal by David Martinez

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Title: Image Guided High Precision Robotic Positioning in MRI for Medical Applications

Date: Tuesday, December 5th

Time: 10:00 am – 12:00 pm ET

Location (in-person): GTMI Auditorium

Location (remote): Click here to join the meeting

      Meeting ID: 239 038 384 563

      Passcode: 2BmTLv


Daniel Enrique Martinez

Robotics PhD Student

George W. Woodruff School of Mechanical Engineering

Georgia Institute of Technology



Dr. Jun Ueda (Advisor)

Dr. Ai-Ping Hu

Dr. John Oshinski

Dr. F. Levent Degertekin

Dr. Yue Chen



Magnetic Resonance Imaging (MRI) is a powerful diagnostic tool that offers advanced visualization of human tissue, increasingly used to guide medical procedures such as biopsies and interventions. Nevertheless, navigation in the MRI environment remains challenging due to material, actuator, and sensor restrictions as well as scan time and cost of use. This work presents methods for ensuring high precision robotic positioning in MRI for use in emerging applications through three distinct aims. In the first aim, an MRI-analogous test bench implementing Position Sensitive Devices (PSDs) is established to measure the positioning performance of a previously developed MRI compatible robot, circumventing limitations of MRI resolution and scan time, validating the capability of MRI guided robot navigation methods. In the second aim, the validated high-precision navigation method is leveraged to enable the application of multi-image Super Resolution (SR) algorithms to construct enhanced resolution in-plane MRI slices, leading to improved positioning precision exceeding the limits of the native MRI resolution. In the third aim, mechanical characterization of a non-Newtonian fluid will be conducted through experimental modelling to compensate for resistive forces when the robot end-effector navigates through a complex fluid medium. Successful completion of this project will enable novel procedures in MRI requiring high positioning accuracy.



  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:11/27/2023
  • Modified By:Tatianna Richardson
  • Modified:11/27/2023



Target Audience