Towards synergistic control of hands-on needle insertion with automated needle steering for MRI-guided prostate interventions

Author(s):  
Marek Wartenberg ◽  
Niravkumar Patel ◽  
Gang Li ◽  
Gregory S. Fischer
2016 ◽  
Vol 01 (01) ◽  
pp. 1640007 ◽  
Author(s):  
Mohsen Khadem ◽  
Carlos Rossa ◽  
Ron S. Sloboda ◽  
Nawaid Usmani ◽  
Mahdi Tavakoli

In needle-based medical procedures, beveled tip flexible needles are steered inside soft tissue to reach the desired target locations. In this paper, we have developed an autonomous image-guided needle steering system that enhances targeting accuracy in needle insertion while minimizing tissue trauma. The system has three main components. First is a novel mechanics-based needle steering model that predicts needle deflection and accepts needle tip rotation as an input for needle steering. The second is a needle tip tracking system that determines needle deflection from the ultrasound images. The needle steering model employs the estimated needle deflection at the present time to predict needle tip trajectory in the future steps. The third component is a nonlinear model predictive controller (NMPC) that steers the needle inside the tissue by rotating the needle beveled tip. The MPC controller calculates control decisions based on iterative optimization of the predictions of the needle steering model. To validate the proposed ultrasound-guided needle steering system, needle insertion experiments in biological tissue phantoms are performed in two cases–with and without obstacle. The results demonstrate that our needle steering strategy guides the needle to the desired targets with the maximum error of 2.85[Formula: see text]mm.


2006 ◽  
Vol 18 (5) ◽  
pp. 643-649
Author(s):  
Yuji Wakasa ◽  
◽  
Masato Oka ◽  
Kanya Tanaka ◽  
Masami Fujii ◽  
...  

Needle insertion in stereotactic brain surgery, such as electrode implantation for Parkinson’s disease, requires highly precise positioning control. MRI-guided robots are practical and promising in realizing safe, precise stereotactic brain surgery, but such robots must meet numerous constraints on component materials due to the strong magnetic field that MRI generates. We developed a needle-insertion robot for MRI-guided surgery taking into account such constraints.


2021 ◽  
Vol 158 ◽  
pp. S73-S74
Author(s):  
M. Moerland ◽  
A. van Lier ◽  
L. van Schelven ◽  
M. van Son ◽  
M. Peters ◽  
...  

Author(s):  
Arefeh Boroomand ◽  
Mahdi Tavakoli ◽  
Ron Sloboda ◽  
Nawaid Usmani

This paper is concerned with deriving a dynamic model of a moderately flexible needle inserted into soft tissue, where the model's output is the needle deflection. The main advantages of the proposed dynamic modeling approach are that the presented model structure involves parameters that are all measurable or identifiable by simple experiments and that it considers the same inputs that are currently used in the clinical practice of manual needle insertion. Conventional manual needle insertion suffers from the fact that flexible needles bend during insertion and their trajectories often vary from those planned, resulting in positioning errors. Enhancement of needle insertion accuracy via robot-assisted needle steering has received significant attention in the past decade. A common assumption in previous research has been that the needle behavior during insertion can be adequately described by static models relating the needle's forces and torques to its deflection. For closed-loop control purposes, however, a dynamic model of the flexible needle in soft tissue is desired. In this paper, we propose a Lagrangian-based dynamic model for the coupled needle/tissue system, and analyze the response of the dynamic system. Steerability (controllability) analysis is also performed, which is only possible with a dynamic model. The proposed dynamic model can serve as a cornerstone of future research into designing dynamics-based control strategies for closed-loop needle steering in soft tissue aimed at minimizing position error.


Author(s):  
Reza Seifabadi ◽  
Sang-Eun Song ◽  
Axel Krieger ◽  
Nathan Bongjoon Cho ◽  
Junichi Tokuda ◽  
...  

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