Toward Certifiable Motion Planning for Medical Steerable Needles

Abstract

Medical steerable needles can move along 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies and localized therapy delivery for cancer. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable motion planner for steerable needles. We introduce the first motion planner for steerable needles that offers a guarantee, under clinically appropriate assumptions, that it will, in finite time, compute an exact, obstacle-avoiding motion plan to a specified target, or notify the user that no such plan exists. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. Compared to state-of-the-art steerable needle motion planners (none of which provide any completeness guarantees), we demonstrate that our new resolution-complete motion planner computes plans faster and with a higher success rate.

Planning of Medical Flexible Needle Motion in Effective Area of Clinical Puncture

“…Most of the RCS* in Figure 9 a,b are below the RCS or RCS* curves, due to the different sample sizes of each algorithm and the 1 s per planning time frame, which we abandoned to estimate the total cost by integrating the curves with the x-axis. Critically, Meng Yu Fu’s experiments [ 15 ] have shown that RCS* exhibits significant performance in terms of actual total consumption cost. The average and cost before reaching the lowest of the six groups in Table 2 and Table 3 have been integrated into Table 4 .…”

“…Meng Yu Fu et al [ 15 ] presented the first resolution-complete but not the resolution-optimal manipulable needle motion planner (RCS), for steerable needles. If there is a puncture path, the RCS will find motion planning in a limited time, provided the parameter resolution is good enough, but it does not guarantee motion planning costs.…”

“…The needle is subject to resistance from soft tissues, which leads to changes in the trajectory of the needle movement. Therefore, we assume that the special material of flexible needles used [ 15 , 18 ] is sufficiently flexible. The needle tip bends with maximum curvature as it passes through the tissue, and the needle tail trajectory strictly follows the needle tip trajectory, with and remaining constant.…”

Insights into Infusion-Based Targeted Drug Delivery in the Brain: Perspectives, Challenges and Opportunities

“…Besides more recent attempts exploiting evolutionary strategies [ 165 ] or machine learning techniques [ 166 ], the majority of the state-of-the-art planners are graph-based methods, such as A* [ 167 , 168 ], or sampling-based methods, such as Rapidly exploring Random Trees (RRT) [ 169 , 170 , 171 , 172 ] and Adaptive Fractal Trees (AFT) [ 173 , 174 ]. In [ 175 ], the authors presented a new searched-based planner based on [ 176 , 177 ] for steerable needles that guarantees completeness under some clinically reasonable assumptions. This is important because having the formal proof that if a plan exists, it will be found and if it does not exist, the user will be notified may prove to be an important aspect for medical certification.…”