Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics最新文献

Accurate needle placement plays a key role in success of prostate biopsy and brachytherapy. During percutaneous interventions, the prostate gland rotates and deforms which may cause significant target displacement. In these cases straight needle trajectory is not sufficient for precise targeting. Although needle spinning and fast insertion may be helpful, they do not entirely resolve the issue. We propose robot-assisted bevel-tip needle steering under MRI guidance as a potential solution to compensate for the target displacement. MRI is chosen for its superior soft tissue contrast in prostate imaging. Due to the confined workspace of the MRI scanner and the requirement for the clinician to be present inside the MRI room during the procedure, we designed a MRI-compatible 2-DOF haptic device to command the needle steering slave robot which operates inside the scanner. The needle steering slave robot was designed to be integrated with a previously developed pneumatically actuated transperineal robot for MRI-guided prostate needle placement. We describe design challenges and present the conceptual design of the master and slave robots and the associated controller.

In this paper we discuss two possible strategies of movement control that can be used by stroke survivors during rehabilitation robotics training. To perform a reaching task in a minimally assistive force field, subjects either can move following the trajectory provided by the assistive force or they can use an internal representation of a minimum jerk trajectory from their starting position to the target. We used the stiffness and damping values directly estimated from the experimental data to simulate the trajectories that result by taking into account both hypotheses. The comparison of the simulated results with the data collected on four hemiparetic subjects supports the hypothesis that the central nervous system (CNS) is still able to correctly plan the movement, although a normal execution is impaired.

Motor impairments seems to play an important role in neurodevelopmental disorders such as autism spectrum disorders (ASD). Early detection of motor abnormalities during first years of life, may give important information regarding whether a child may receive a later diagnosis of Autism: for this reason an objective assessment of motor performance is crucial. While there are several technological solutions suitable to this end, they often require highly structured environments. In this work we propose the use of a magneto-inertial platform to study early motor performance between 12-36 months of age suitable to be used in non-structured environment.

Magnetic Resonance Imaging (MRI) guided robotic interventions have been introduced in order to advance prostate cancer detection and treatment. To overcome problems of such robotic interventions, we have been developing a pneumatically actuated MRI-compatible modular robotic system for MRI-guided transperineal prostate intervention and its interventional procedure. For system evaluation, a series of experiments have been conducted and this paper reports a needle insertion experiment using prostate phantom and patient mockup trials. The needle insertion experiment resulted in noticeable consistent error in one direction, which we will investigate further. Nonetheless, patient mockup experiences suggest that the modular robotic system and its interventional procedure are well integrated and implemented in clinical environment.

In retinal microsurgery, surgeons are required to perform micron scale maneuvers while safely applying forces to the retinal tissue that are below sensory perception. Real-time characterization and precise manipulation of this delicate tissue has thus far been hindered by human limits on tool control and the lack of a surgically compatible endpoint sensing instrument. Here we present the design of a new generation, cooperatively controlled microsurgery robot with a remote center-of-motion (RCM) mechanism and an integrated custom micro-force sensing surgical hook. Utilizing the forces measured by the end effector, we correct for tool deflections and implement a micro-force guided cooperative control algorithm to actively guide the operator. Preliminary experiments have been carried out to test our new control methods on raw chicken egg inner shell membranes and to capture useful dynamic characteristics associated with delicate tissue manipulations.

Bevel-tip steerable needles for minimally invasive medical procedures can be used to reach clinical targets that are behind sensitive or impenetrable areas and are inaccessible to straight, rigid needles. We present a fast algorithm that can compute motion plans for steerable needles to reach targets in complex, 3D environments with obstacles at interactive rates. The fast computation makes this method suitable for online control of the steerable needle based on 3D imaging feedback and allows physicians to interactively edit the planning environment in real-time by adding obstacle definitions as they are discovered or become relevant. We achieve this fast performance by using a Rapidly Exploring Random Tree (RRT) combined with a reachability-guided sampling heuristic to alleviate the sensitivity of the RRT planner to the choice of the distance metric. We also relax the constraint of constant-curvature needle trajectories by relying on duty-cycling to realize bounded-curvature needle trajectories. These characteristics enable us to achieve orders of magnitude speed-up compared to previous approaches; we compute steerable needle motion plans in under 1 second for challenging environments containing complex, polyhedral obstacles and narrow passages.

This paper describes a real-time gait mode intent recognition approach for the supervisory control of a powered transfemoral prosthesis. The proposed approach infers user intent by recognizing patterns in the prosthesis sensor's signals in real-time, eliminating the need for sound-side instrumentation and allowing fast mode switching. Simple time based features extracted from frames of prosthesis signals are reduced to lower dimensions. Gaussian Mixture Models are trained using an experimental database for gait mode classification. A voting scheme is applied as a post-processing step to increase the robustness of decision making. The effectiveness of the proposed method is shown via gait experiments on a treadmill with a healthy subject using an able bodied adapter.

Flexible, tip-steerable needles promise to enhance physicians' 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 intra-operative 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 180° rotations of the needle shaft. This preplanned 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 bevel-tip 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 180° 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.

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.

Long, flexible, bevel-tip needles curve during insertion into tissue, and rotations of the needle base reorient the tip to steer subsequent insertions. Friction between the tissue and the needle shaft, however, can cause a severe discrepancy between the needle base and tip angles. In this paper, I demonstrate an algorithm to properly align the entire length of the needle using torque measured at the base. My algorithm uses several intermediate base rotations to align the orientation of points along the shaft with the desired angle, with minimal remaining torque exerted by the base. I performed an experimental validation with four angle sensors attached to the needle throughout the tissue. My compensation algorithm decreased the lag throughout the needle by up to 88%.