A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

Abstract

Percutaneous needle insertion is a minimally invasive surgery with broad medical application prospects, such as biopsy and brachytherapy. However, the currently adopted rigid needles have limitations, as they cannot bypass obstacles or correct puncture deviations and can only travel along a straight path. Bevel-tip flexible needles are increasingly being adopted to address these issues, owing to their needle body's ease of deformation and bending. Successful puncture of flexible needles relies on accurate models and path planning, ensuring the needle reaches the target while avoiding vital tissues. This review investigates puncture models and path-planning algorithms by reviewing recent literature, focusing on the path-planning part. According to the literature, puncture models can be divided into three types: mechanical, finite element method (FEM), and kinematic models, while path-planning algorithms are categorized and discussed following the division used for mobile robots, which differs from the conventional approach for flexible needles-an innovation in this review. This review systematically summarizes the following categories: graph theory search, sampling-based, intelligent search, local obstacle avoidance, and other algorithms, including their implementation, advantages, and disadvantages, to further explore the potential to overcome obstacles in path planning for minimally invasive puncture needles. Finally, this study proposes future development trends in path-planning algorithms, providing possible directions for subsequent research for bevel-tipped flexible needles. This research aims to provide a resource for researchers to quickly learn about common path-planning algorithms, their backgrounds, and puncture models.

Keywords: Bevel-tipped flexible needle; Minimally invasive surgery; Obstacle avoidance; Path planning; Puncture model.

Fig. 1

Classification chart of flexible needle path planning algorithms.

Fig. 2

Mechanical model of a bevel-tipped flexible needle: (a) the geometric model of the flexible needle and liver [31]; (b) mechanical model of a flexible needle puncturing soft tissue [25]; (c) force analysis of the interaction between needle and trachea tissue during puncturing [34].

Fig. 3

FEM model of flexible needle puncture: (a) interactive simulation of needle puncturing in a planar environment [35]; (b) finite element simulation of robot–controlled needle insertion into tissue [38]; (c) FEM model for needle insertion analysis [39]; (d) finite element simulation of puncturing process for needle and trachea tissue [34].

Fig. 4

Flexible needle kinematic model: (a) multiple arcs path; (b) spiral line path; (c) straight-line path; (d) Nonholonomic bicycle model proposed by Webster et al. [9]; (e) Simplified bicycle model [47].

Fig. 5

Path planning based on the RRT algorithm: (a) the search graph of the RRT algorithm; (b) path planning based on back-chaining the RRT algorithm [66]; (c) path planning for multiple target points [67]; (d) path planning based on the I-RRT algorithm [49]; (e) the simulation results combined with greedy heuristic and reachable guidance RRT algorithm [74]; (f) the left image depicts the path before smoothing, and the right image depicts the final path with Bezier Curve Smoothing [78].

Fig. 6

Path planning based on PRM algorithm: (a) paths planned by Lobaton et al. [80]; (b) path planning based on RRM algorithm [81].

Fig. 7

Genetic Algorithm: (a) the process of the Genetic Algorithm; (b) the experiment of Wilz et al. [86].

Fig. 8

Ant foraging diagram for ACO.

Fig. 9

Path planning based on PSO algorithm: (a) path planning based on MOPSO algorithm [93], (b) path planning based on BFL-PSO algorithm [94], (c) path planning based on PSO algorithm [95], and (d) path planning based on RRT algorithm and PSO algorithm [96].

Fig. 10

Path planning based on APF algorithm: (a) the path planned by Zhang et al. [77]; (b) the path planned by Zhao et al. [102].

Fig. 11

Paths planned by Alterovitz et al. [110]: (a) Path planning based on MDP method; (b) Path planning based on SMRM algorithm; (c) Path planning based on image-guided method.

Fig. 12

Path planning based on deep learning: (a) path planning based on the DQN algorithm [114]; (b) path planning based on CT guidance [21].

Fig. 13

(a) Path planning based on POP algorithm [116]; (b) Paths planned by Duindam et al. [44].