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Steerable Needle Planner

Update 02/14/2022

Extended code for IEEE International Conference on Robotics and Automation (ICRA) 2022 paper Resolution-Optimal Motion Planning for Steerable Needles (to appear). [arXiv]

Original

Code for Robotics: Science and Systems (RSS) 2021 paper Toward Certifiable Motion Planning for Medical Steerable Needles. [Paper]

Steerable Needle Planner efficiently produces motion plans for medical steerable needles, considering constraints including maximum curvature, obstacles, and etc.

Steerable Needle Planner is based on Motion Planning Templates (MPT), a collection of C++ template classes for fast, parallel, robot-specific motion planning.

Motion Planning for Steerable Needles

Steerable needles are highly flexible medical devices able to follow 3D curvilinear trajectories inside the human body, reaching clinically significant targets while safely avoiding critical anatomical structures. Compared with traditional rigid-medical instruments, steerable needles can reduce a patient’s trauma, increase safety, and provide minimally invasive access to targets that were previously inaccessible. Steerable needles have been considered in a wide range of diagnostic and treatment procedures including biopsy, and radioactive seed implantation for cancer treatment.

Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability and ability to accurately and precisely reach targets. Automation is critical to harnessing the full potential of these needles since the non-holonomic constraints on the needle’s 3D motion coupled with the cluttered nature of anatomical environments make direct manual control un-intuitive and impractical for human operators. To automate steerable needle procedures, physicians first obtain a medical image (such as a CT scan or MRI) of the relevant anatomy, from which we can segment (manually or automatically) the relevant anatomy, including the target to reach and obstacles to avoid. The next key ingredient to the automation of steerable needle procedures is motion planning, which requires computing feasible motions to steer the needle safely around the anatomical obstacles and to the target.

Requirements

  • C++17 compatible compiler (GCC 7+ for Linux, Clang 5+ for maxOS)
  • CMake 3.8+
  • Eigen 3.3+
  • Boost 1.68+
  • [For visualization only] Python 3.6-3.8
  • [For visualization only] Open3d

Installing Dependencies on Ubuntu with apt

sudo apt install cmake libeigen3-dev libboost-all-dev [python3.8]
[pip3 install open3d]

Installing Dependencies on macOS with Homebrew

brew install cmake eigen boost [[email protected]]
[pip3 install open3d]

Usage

Download

git clone [email protected]:UNC-Robotics/steerable-needle-planner.git [{YOUR_LOCAL_REPO}]
cd {YOUR_LOCAL_REPO}
git submodule update --init --recursive

Build

mkdir -p {YOUR_LOCAL_REPO}/build
cd {YOUR_LOCAL_REPO}/build
cmake ..
make

Run

This repository contains several different needle planners:

  • An RRT-based planner (referred to as RRT) initially proposed by Patil et al. [1]
  • A planner based on AO-RRT [2] that is adapted for steerable needles.
  • A search-based resolution-complete planner (referred to as RCS) initially proposed by Fu et al. [3]
  • A search-based resolution-optimal planner (referred to as RCS*) initially proposed by Fu et al. [4]

There are several test applications already provided, they all read from {YOUR_LOCAL_REPO}/data/input/* for environment information, needle parameters, and start/goal poses. By default, the planner saves the search tree to data/output/{DATE_AND_TIME}_ptcloud.txt and saves the best solution plan to data/output/{DATE_AND_TIME}_interp.txt.

Before running any of the test applications, run

mkdir -p {YOUR_LOCAL_REPO}/data/output

since the test applications will save results in the above directory.

  • Plan from start to goal using the RRT planner:
cd {YOUR_LOCAL_REPO}/build
./app/rrt [if_constrain_goal_orientation] [if_multi_threading] [random_seed] [tag]
  • Plan from start to goal using the AO-RRT planner:
cd {YOUR_LOCAL_REPO}/build
./app/aorrt [if_constrain_goal_orientation] [if_multi_threading] [random_seed] [tag]
  • Plan from start to goal using the RCS planner:
cd {YOUR_LOCAL_REPO}/build
./app/rcs [if_constrain_goal_orientation] [if_multi_threading] [random_seed] [tag]
  • Plan from start to goal using the RCS* planner:
cd {YOUR_LOCAL_REPO}/build
./app/rcs_star [if_constrain_goal_orientation] [if_multi_threading] [random_seed] [tag]

For the above test applications, the planner will run for 1 second and collect all solution plans generated. Checkout include/test_utils.h for different termination conditions.

  • Plan from start (with no orientation constraint) to goal regions, using the RRT planner:
cd {YOUR_LOCAL_REPO}/build
./app/rrt_spreading [if_multi_threading] [random_seed] [tag]
  • Plan from start (with no orientation constraint) to goal regions, using the AO-RRT planner:
cd {YOUR_LOCAL_REPO}/build
./app/aorrt_spreading [if_multi_threading] [random_seed] [tag]
  • Plan from start (with no orientation constraint) to goal regions, using the RCS planner:
cd {YOUR_LOCAL_REPO}/build
./app/rcs_spreading [if_multi_threading] [random_seed] [tag]

For the above test applications, the planner will run for 50 seconds and collect all solution plans that get close enough (within 3mm) to the goal points.

Quick Visualization

The search tree and plans produced by a planner can be visualized as point clouds using scripts/ptcloud_vis.py. It requires Open3d. Currently, Open3d supports python 3.6, 3.7, and 3.8, but does not support python 3.9. Use the following command to run it:

python3 {YOUR_LOCAL_REPO}/scripts/ptcloud_vis.py ptcloud_0 [ptcloud_1] [ptcloud_2] ...

where ptcloud_x are the files saving the point clouds. For example, python3 {YOUR_LOCAL_REPO}/scripts/ptcloud_vis.py {YOUR_LOCAL_REPO}/data/input/goal_regions.txt will visualize all goal points as a point cloud.

References

[1] Patil, S., Burgner, J., Webster, R.J. and Alterovitz, R., 2014. Needle steering in 3-D via rapid replanning. IEEE Transactions on Robotics, 30(4), pp.853-864.

[2] Hauser, K. and Zhou, Y., 2016. Asymptotically optimal planning by feasible kinodynamic planning in a state–cost space. IEEE Transactions on Robotics, 32(6), pp.1431-1443.

[3] Fu, M., Salzman, O. and Alterovitz, R., 2021. Toward Certifiable Motion Planning for Medical Steerable Needles. Robotics science and systems: online proceedings, 2021.

[4] Fu, M., Solovey, K., Salzman, O. and Alterovitz, R., 2022. Resolution-Optimal Motion Planning for Steerable Needles. IEEE International Conference on Robotics and Automation, 2022.

Citation

If you use this source code, please cite the following papers accordingly:

@inproceedings{Fu2021_RSS,
    author    = {Mengyu Fu and Oren Salzman and Ron Alterovitz},
    title     = {{Toward Certifiable Motion Planning for Medical Steerable Needles}},
    booktitle = {Proceedings of Robotics: Science and Systems},
    year      = {2021},
    address   = {Virtual},
    month     = {July},
    doi       = {10.15607/RSS.2021.XVII.081}
}

@inproceedings{Fu2022_ICRA,
    author={Mengyu Fu and Kiril Solovey and Oren Salzman and Ron Alterovitz},
    title={{Resolution-Optimal Motion Planning for Steerable Needles}},
    booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)},
    year={2022},
    volume={},
    number={},
    pages={9652-9659}
}