Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Endovascular intervention is limited by two-dimensional intraoperative imaging and prolonged procedure times in the presence of complex anatomies. Robotic catheter technology could offer benefits such as reduced radiation exposure to the clinician and improved intravascular navigation. Incorporating three-dimensional preoperative imaging into a semiautonomous robotic catheterization platform has the potential for safer and more precise navigation. This paper discusses a semiautonomous robotic catheter platform based on previous work (Rafii-Tari et al., in: MICCAI2013, pp 369-377. https://doi.org/10.1007/978-3-642-40763-5_46 , 2013) by proposing a method to address anatomical variability among aortic arches. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among patient-specific anatomies.

Authors

  • Wenqiang Chi
    Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK. wenqiang.chi10@imperial.ac.uk.
  • Jindong Liu
  • Hedyeh Rafii-Tari
    The Hamlyn Centre for Robotic Surgery, Imperial College London, Level 4, Bessemer Building, South Kensington Campus, London, SW7 2AZ, UK. h.rafii-tari11@imperial.ac.uk.
  • Celia Riga
    Academic Division of Surgery, Imperial College London, London, UK.
  • Colin Bicknell
    Academic Division of Surgery, Imperial College London, London, UK.
  • Guang-Zhong Yang
    Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China. dgunning@fb.com gzyang@sjtu.edu.cn.