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

By
Wenqiang Chi
Jindong Liu
Hedyeh Rafii-Tari
Celia Riga
Colin Bicknell
Guang-Zhong Yang
April 12, 2018
0 min

International Journal Of Computer Assisted Radiology And Surgery

1

Endovascular intervention is a key treatment for vascular pathologies, utilizing robotic systems for improved catheter navigation.
2

Robot-assisted catheterization offers advantages like enhanced stability, precision, and reduced operator radiation exposure.
3

Learning from demonstration (LfD) frameworks can automate catheterization tasks by replicating expert motion patterns.
4

The proposed method incorporates anatomical information to optimize robotic catheter trajectories for specific vascular anatomies.
5

Testing shows that the robotic approach yields smoother catheter paths and reduces contact forces, minimizing complication risks.

Original Source(s)

International Journal Of Computer Assisted Radiology And Surgery

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

by Wenqiang Chi, Jindong Liu, Hedyeh Rafii-Tari, Celia Riga, Colin Bicknell, Guang-Zhong Yang
April 12, 2018

Related Content

Cedars-Sinai Pulse

About the Vascular Surgery Fellowship Program | Cedars-Sinai Academic Medicine

The Vascular Surgery Fellowship, which is accredited by the Accreditation Council for Graduate Medical Education (ACGME), is a program of Cedars-Sinai, an academic medical center renowned for vascular care, research and education. Cedars-Sinai is consistently ranked one of the nation's best hospitals by U.S. News & World Report.