A Proposal for Realistic Modeling and Shared Control Integration in Endovascular Surgery

MSc assignment

Advanced endovascular operations are necessary since cardiovascular disease, the most significant cause of death in the Western world, is mainly caused by vascular disorders. These procedures, which include methods such as embolization, ablation, and stenting, necessitate a high level of skill development. Medical simulation, on the other hand, shows promise as a means of reducing this learning curve and allowing trainees to practice without putting patients’ lives in jeopardy. The need for better treatment approaches becomes clear when one considers that cardiovascular diseases account for 17.9 million deaths worldwide each year, or 31% of all deaths. Compared to open surgery, endovascular procedures have several benefits, such as shorter recovery periods, less need for general anaesthesia, and decreased fatality rates. However, these methods need the expert navigation of pre-shaped catheters through complex vasculature, which can be difficult even for experienced practitioners. 

My project's goal is to build a model for a realistic Unity3D simulation using MRI imaging or X-rays. Using MRI data, this technique creates incredibly lifelike Unity3D medical simulations that provide trainees and medical professionals with an immersive learning environment. 

Furthermore, the objective is to create a working prototype of autonomous control, which will serve as a foundation for the eventual adoption of shared control methods.

Thesis project:

  • Get a realistic simulation on Unity3D using magnetic resonance imaging or X-rays.
  • Shared control: implement an autonomous control to move from a certain artery/vein to another.

Relevant References:

  1. Kundrat Dennis Bos Jochem and Dagnino Giulio. “Towards an Action Recognition Framework for Endovascular Surgery”. In: (2023). DOI: 10.1109/EMBC40787.2023.10341057. URL: https://ieeexplore. ieee.org/document/10341057 
  2. D. Nguyen et al. “End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention”. In: (2020)
  3. Dennis Kundrat; Giulio Dagnino; Trevor M. Y. Kwok; Mohamed E. M. K. Abdelaziz; Wenqiang Chi; Anh Nguyen; Celia Riga; Guang-Zhong Yang. “An MR-Safe Endovascular Robotic Platform: Design, Control, and Ex-Vivo Evaluation”. In: (2020). URL: https://ieeexplore.ieee.org/document/9376657
  4. G. Dagnino; J. Liu; M. E. M. K. Abdelaziz; W. Chi; C. Riga; G.-Z. Yang. “Haptic Feedback and Dynamic Active Constraints for Robot-Assisted Endovascular Catheterization”. In: (2018). URL: https://ieeexplore.ieee. org/document/8593628
  5. Giulio Dagnino; Dennis Kundrat; Trevor M. Y. Kwok; Mohamed E. M. K. Abdelaziz; Wenqiang Chi; Anh Nguyen; Celia Riga; Guang-Zhong Yang. “In-Vivo Validation of a Novel Robotic Platform for Endovascular Intervention”. In: IEEE Transactions on Biomedical Engineering 70 (2023), pp. 1786–1794. DOI: 10.1109/TBME.2022. 3227734. URL: https://ieeexplore.ieee.org/document/9976296
  6. Wenqiang Chi; Giulio Dagnino; Trevor M. Y. Kwok; Anh Nguyen; Dennis Kundrat; Mohamed E. M. K. Abdelaziz; Celia Riga; Colin Bicknell; Guang-Zhong Yang. “Collaborative Robot-Assisted Endovascular Catheterization with Generative Adversarial Imitation Learning”. In: (2020). URL: https://ieeexplore.ieee.org/ document/9196912