Kyle B. Reed

Mechanics of Flexible Needles Robotically Steered through Soft Tissue

The tip asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. This enables robotic needle steering, which can be used in medical procedures to reach subsurface targets inaccessible by straight-line trajectories. However, accurate path planning and control of needle steering require models of needle-tissue interaction. Previous kinematic models required empirical observations of each needle and tissue combination in order to fit model parameters. This study describes a mechanics-based model of robotic needle steering, which can be used to predict needle behavior and optimize system design based on fundamental mechanical and geometrical properties of the needle and tissue. We first present an analytical model for the loads developed at the tip, based on the geometry of the bevel edge and material properties of soft-tissue simulants (gels). We then present a mechanics-based model that calculates the deflection of a bevel-tipped needle inserted through a soft elastic medium. The model design is guided by microscopic observations of needle-gel interactions. The energy-based formulation incorporates tissue-specific parameters, and the geometry and material properties of the needle. Simulation results follow similar trends (deflection and radius of curvature) to those observed in experimental studies of robotic needle insertion.

Robot-Assisted Needle Steering

Needle insertion is a critical aspect of many medical treatments, diagnostic methods, and scientific studies, and is considered to be one of the simplest and most minimally invasive medical procedures. Robot-assisted needle steering has the potential to improve the effectiveness of existing medical procedures and enable new ones by allowing increased accuracy through more dexterous control of the needle-tip path and acquisition of targets not accessible by straight-line trajectories. In this article, we describe a robot-assisted needle-steering system that uses three integrated controllers: a motion planner concerned with guiding the needle around obstacles to a target in a desired plane, a planar controller that maintains the needle in the desired plane, and a torsion compensator that controls the needle-tip orientation about the axis of the needle shaft.

Haptically Linked Dyads: Are Two Motor-Control Systems Better Than One?

Are Two Motor-Control Systems Better Than One? Kyle Reed, Michael Peshkin, Mitra J. Hartmann, Marcia Grabowecky, James Patton, and Peter M. Vishton Department of Mechanical Engineering, Northwestern University; Department of Biomedical Engineering, Northwestern University; Department of Psychology, Northwestern University; Rehabilitation Institute of Chicago, Chicago, Illinois; and Department of Psychology, College of William and Mary

Needle-tissue interaction forces for bevel-tip steerable needles

The asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. As a first step toward modeling the mechanics of deflection of the needle, we determine the forces at the bevel tip. In order to find the forces acting at the needle tip, we measure rupture toughness and nonlinear material elasticity parameters of several soft tissue simulant gels and chicken tissue. We incorporate these physical parameters into a finite element model that includes both contact and cohesive zone models to simulate tissue cleavage. We investigate the sensitivity of the tip forces to tissue rupture toughness, linear and nonlinear tissue elasticity, and needle tip bevel angle. The model shows that the tip forces are sensitive to the rupture toughness. The results from these studies contribute to a mechanics-based model of bevel-tip needle steering, extending previous work on kinematic models.

Integrated planning and image-guided control for planar needle steering

Flexible, tip-steerable needles promise to enhance physicianspsila abilities to accurately reach targets and maneuver inside the human body while minimizing patient trauma. Here, we present a functional needle steering system that integrates two components: (1) a patient-specific 2D pre- and intraoperative planner that finds an achievable route to a target within a planar slice of tissue (Stochastic Motion Roadmap), and (2) a low-level image-guided feedback controller that keeps the needle tip within that slice. The planner generates a sequence of circular arcs that can be realized by interleaving pure insertions with 180deg rotations of the needle shaft. This pre-planned sequence is updated in realtime at regular intervals. Concurrently, the low-level image-based controller servos the needle to remain close to the desired plane between plan updates. Both planner and controller are predicated on a previously developed kinematic nonholonomic model of beveltip needle steering. We use slighly different needles here that have a small bend near the tip, so we extend the model to account for discontinuities of the tip position caused by 180deg rotations. Further, during large rotations of the needle base, we maintain the desired tip angle by compensating for torsional compliance in the needle shaft, neglected in previous needle steering work. By integrating planning, control, and torsion compensation, we demonstrate both accurate targeting and obstacle avoidance.

Robotic Needle Steering: Design, Modeling, Planning, and Image Guidance

This chapter describes how advances in needle design, modeling, planning, and image guidance make it possible to steer flexible needles from outside the body to reach specified anatomical targets not accessible using traditional needle insertion methods. Steering can be achieved using a variety of mechanisms, including tip-based steering, lateral manipulation, and applying forces to the tissue as the needle is inserted. Models of these steering mechanisms can predict needle trajectory based on steering commands, motivating new preoperative path planning algorithms. These planning algorithms can be integrated with emerging needle imaging technology to achieve intraoperative closed-loop guidance and control of steerable needles.

Evaluation of robotic needle steering in ex vivo tissue

Insertion velocity, tip asymmetry, and shaft diameter may influence steerable needle insertion paths in soft tissue. In this paper we examine the effects of these variables on needle paths in ex vivo goat liver, and demonstrate practical applications of robotic needle steering for ablation, biopsy, and brachytherapy. All experiments were performed using a new portable needle steering robot that steers asymmetric-tip needles under fluoroscopic imaging. For bevel-tip needles, we found that larger diameter needles resulted in less curvature, i.e. less steerability, confirming previous experiments in artificial tissue. The needles steered with radii of curvature ranging from 3.4 cm (for the most steerable pre-bent needle) to 2.97m (for the least steerable bevel needle). Pre-bend angle significantly affected needle curvature, but bevel angle did not. We hypothesize that biological tissue characteristics such as inhomogeneity and viscoelasticity significantly increase path variability. These results underscore the need for closed-loop image guidance for needle steering in biological tissues with complex internal structure.

Replicating Human-Human Physical Interaction

Machines might physically interact with humans more smoothly if we better understood the subtlety of human-human physical interaction. We recently reported that two people working cooperatively on a physical task will quickly negotiate an emergent strategy: typically subjects formed a temporal specialization such that one member commands the early parts of motion and the other the late parts. In our study, we replaced one of the humans with a robot programmed to perform one of the typical human specialized roles. We expected the remaining human to adopt the complementary specialized role. Subjects did believe that they were interacting with another human but did not adopt a specialized behavior as subjects would when physically working with another human; our negative result suggests a very subtle negotiation takes place in human-human physical interaction.

Asymmetric passive dynamic walker

The objective of this research is to better understand the dynamics of gait asymmetry in humans with central nervous system damage, such as stroke, by using a model of a passive dynamic walker (PDW). By changing the mass, mass location, knee location, and leg length of one leg while leaving the parameters of the other leg unchanged, we show that stable asymmetric walking patterns exist for PDW models. The asymmetric PDW model shows several stable walking patterns that have a single, double, and quadruple repeat pattern where the step lengths between the two legs differ by over 15%. This model will allow an analysis of the passive dynamics of walking separate from the cognitive control in asymmetric human walking to test different gait rehabilitation hypotheses.

Estimation of model parameters for steerable needles

Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.