3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles

© 2016 IEEE. Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this letter, we propose to take advantage of the architecture of graphics processing units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed adaptive fractal trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional (3-D) simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation

Design and evaluation of a computed tomography (CT)-compatible needle insertion device using an electromagnetic tracking system and CT images

Purpose Percutaneous needle insertion procedures are commonly used for diagnostic and therapeutic purposes. Although current technology allows accurate localization of lesions, they cannot yet be precisely targeted. Lung cancer is the most common cause of cancer-related death, and early detection reduces the mortality rate. Therefore, suspicious lesions are tested for diagnosis by performing needle biopsy. Methods In this paper, we have presented a novel computed tomography (CT)-compatible needle insertion device (NID). The NID is used to steer a flexible needle (ϕ0.55mm ϕ0.55mm) with a bevel at the tip in biological tissue. CT images and an electromagnetic (EM) tracking system are used in two separate scenarios to track the needle tip in three-dimensional space during the procedure. Our system uses a control algorithm to steer the needle through a combination of insertion and minimal number of rotations. Results Noise analysis of CT images has demonstrated the compatibility of the device. The results for three experimental cases (case 1: open-loop control, case 2: closed-loop control using EM tracking system and case 3: closed-loop control using CT images) are presented. Each experimental case is performed five times, and average targeting errors are 2.86±1.14 2.86±1.14, 1.11±0.14 1.11±0.14 and 1.94 0.63mm 1.94±0.63mm for case 1, case 2 and case 3, respectively. Conclusions The achieved results show that our device is CT-compatible and it is able to steer a bevel-tipped needle toward a target. We are able to use intermittent CT images and EM tracking data to control the needle path in a closed-loop manner. These results are promising and suggest that it is possible to accurately target the lesions in real clinical procedures in the future

Full 3D motion control for programmable bevel-tip steerable needles

Minimally invasive surgery has been in the focus of many researchers due to its reduced intra- and post-operative risks when compared to an equivalent open surgery approach. In the context of minimally invasive surgery, percutaneous intervention, and particularly, needle insertions, are of great importance in tumour-related therapy and diagnosis. However, needle and tissue deformation occurring during needle insertion often results in misplacement of the needles, which leads to complications, such as unsuccessful treatment and misdiagnosis. To this end, steerable needles have been proposed to compensate for placement errors by allowing curvilinear navigation. A particular type of steerable needle is the programmable bevel-tip steerable needle (PBN), which is a bio-inspired needle that consists of relatively soft and slender segments. Due to its flexibility and bevel-tip segments, it can navigate through 3D curvilinear paths. In PBNs, steering in a desired direction is performed by actuating particular PBN segments. Therefore, the pose of each segment is needed to ensure that the correct segment is actuated. To this end, in this thesis, proprioceptive sensing methods for PBNs were investigated. Two novel methods, an electromagnetic (EM)-based tip pose estimation method and a fibre Bragg grating (FBG)-based full shape sensing method, were presented and evaluated. The error in position was observed to be less than 1.08 mm and 5.76 mm, with the proposed EM-based tip tracking and FBG-based shape reconstruction methods, respectively. Moreover, autonomous path-following controllers for PBNs were also investigated. A closed-loop, 3D path-following controller, which was closed via feedback from FBG-inscribed multi-core fibres embedded within the needle, was presented. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, and active disturbance rejection control (ADRC), which is known for its robustness within hard-to-model environments, were chosen as the control methods. Both linear and nonlinear ADRC were investigated, and the approaches were validated in both ex vivo brain and phantom tissue, with some of the experiments involving moving targets. The tracking error in position was observed to be less than 6.56 mm.Open Acces

Towards Closed-loop, Robot Assisted Percutaneous Interventions under MRI Guidance

Image guided therapy procedures under MRI guidance has been a focused research area over past decade. Also, over the last decade, various MRI guided robotic devices have been developed and used clinically for percutaneous interventions, such as prostate biopsy, brachytherapy, and tissue ablation. Though MRI provides better soft tissue contrast compared to Computed Tomography and Ultrasound, it poses various challenges like constrained space, less ergonomic patient access and limited material choices due to its high magnetic field. Even after, advancements in MRI compatible actuation methods and robotic devices using them, most MRI guided interventions are still open-loop in nature and relies on preoperative or intraoperative images. In this thesis, an intraoperative MRI guided robotic system for prostate biopsy comprising of an MRI compatible 4-DOF robotic manipulator, robot controller and control application with Clinical User Interface (CUI) and surgical planning applications (3DSlicer and RadVision) is presented. This system utilizes intraoperative images acquired after each full or partial needle insertion for needle tip localization. Presented system was approved by Institutional Review Board at Brigham and Women\u27s Hospital(BWH) and has been used in 30 patient trials. Successful translation of such a system utilizing intraoperative MR images motivated towards the development of a system architecture for close-loop, real-time MRI guided percutaneous interventions. Robot assisted, close-loop intervention could help in accurate positioning and localization of the therapy delivery instrument, improve physician and patient comfort and allow real-time therapy monitoring. Also, utilizing real-time MR images could allow correction of surgical instrument trajectory and controlled therapy delivery. Two of the applications validating the presented architecture; closed-loop needle steering and MRI guided brain tumor ablation are demonstrated under real-time MRI guidance

Energy shaping control for robotic needle insertion

This work investigates the use of energy shaping control to reduce deflection in slender beams with tip load and actuation at the base. The ultimate goal of this research is a buckling avoidance strategy for robotic-assisted needle insertion. To this end, the rigid-link model of a flexible beam actuated at the base and subject to tip load is proposed, and an energy shaping approach is employed to construct a nonlinear controller that accounts for external forces. A comparative simulation study highlights the benefits of the proposed approach over a linear control baseline and a simplified nonlinear control

A mechanics-based model for 3D steering of programmable bevel-tip needles

We present a model for the steering of programmable bevel-tip needles, along with a set of experiments demonstrating the 3D steering performance of a new, clinically viable, 4-segment, pre-production prototype. A multi-beam approach, based on Euler-Bernoulli beam theory, is used to model the novel multi-segment design of these needles. Finite element simulations for known loads are used to validate the multi-beam deflection model. A clinically sized (2.5 mm outer diameter), 4-segment programmable bevel-tip needle, manufactured by extrusion of a medical-grade polymer, is used to conduct an extensive set of experimental trials to evaluate the steering model. For the first time, we demonstrate the ability of the 4-segment needle design to steer in any direction with a maximum achievable curvature of 0.0192±0.0014 mm⁻¹. Finite element simulations confirm that the multi-beam approach produces a good model fit for tip deflections, with a root-mean-square deviation (RMSD) in modeled tip deflection of 0.2636 mm. We perform a parameter optimization to produce a best-fit steering model for the experimental trials, with a RMSD in curvature prediction of 1.12×10⁻³ mm⁻¹

SMART IMAGE-GUIDED NEEDLE INSERTION FOR TISSUE BIOPSY

M.S

Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor

The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm