Long-term integration of neuroprosthetic devices is challenged by reactive responses that compromise the brain-device interface. The contribution of physical insertion parameters to immediate damage is not well described. We have developed an ex vivo preparation to capture real-time images of tissue deformation during device insertion using thick tissue slices from rat brains prepared with fluorescently labeled vasculature. Qualitative and quantitative assessments of damage were made for insertions using devices with different tip shapes inserted at different speeds. Direct damage to the vasculature included severing, rupturing and dragging, and was often observed several hundred micrometers from the insertion site. Slower insertions generally resulted in more vascular damage. Cortical surface features greatly affected insertion success; insertions attempted through pial blood vessels resulted in severe tissue compression. Automated image analysis techniques were developed to quantify tissue deformation and calculate mean effective strain. Quantitative measures demonstrated that, within the range of experimental conditions studied, faster insertion of sharp devices resulted in lower mean effective strain. Variability within each insertion condition indicates that multiple biological factors may influence insertion success. Multiple biological factors may contribute to tissue distortion, thus a wide variability was observed among insertions made under the same conditions.
Development of a laparoscopic surgery simulator that delivers high fidelity visual and haptic (force) feedback, based on the physical models of soft tissues, requires the use of empirical data on the mechanical behavior of intra-abdominal organs under the action of external forces. As experiments on live human patients present significant risks, the use of cadavers presents an alternative. We present techniques of measuring and modeling the mechanical response of human cadaveric tissue for the purpose of developing a realistic model. The major contribution of this paper is the development of physics-based models of soft tissues that range from linear elastic models to nonlinear viscoelastic models which are efficient for application within the framework of a real time surgery simulator. To investigate the in situ mechanical, static and dynamic properties of intra-abdominal organs, we have developed a high precision instrument by retrofitting a robotic device from Sensable Technologies (position resolution of 0.03 mm) with a six-axis Nano 17 force-torque sensor from ATI Industrial Automation (force resolution of 1/1280 N along each axis), and used it to apply precise displacement stimuli and record the force response of liver and stomach of 10 fresh human cadavers. The mean elastic modulus of liver and stomach is estimated as 5.9359 kPa and 1.9119 kPa, respectively over the range of indentation depths tested. We have also obtained the parameters of a quasi linear viscoelastic (QLV) model to represent the nonlinear viscoelastic behavior of the cadaver stomach and liver over a range of indentation depths and speeds. The models are found to have an excellent goodness of fit (with R2>0.99). The data and models presented in this paper together with additional ones based on the principles presented in this paper would result in realistic physics-based surgical simulators.
The generation of multimodal virtual environments for surgical training is complicated by the necessity to develop heterogeneous simulation scenarios such as surgical incision, cauterization, bleeding, and smoke generation involving the interaction of surgical tools with soft biological tissues in real time. While several techniques ranging from rapid but nonphysical geometry-based procedures to complex but computationally inefficient finite element analysis schemes have been proposed, none is uniquely suited to solve the digital surgery problem. In this paper we discuss the challenges facing the field of realistic surgery simulation and present a novel point-associated finite field (PAFF) approach, developed specifically to cope with these challenges. Based upon the equations of motion dictated by physics, this technique is independent of the state of matter, geometry and material properties and permits different levels of detail. We propose several specializations of this scheme for various operational complexities. The accuracy and efficiency of this technique is compared with solutions using traditional finite element methods and simulation results are reported on segmented models obtained from the Visible Human Project. BackgroundSurgical teaching has been based traditionally on the preceptor or apprenticeship model, in which the novice surgeon learns with small groups of peers and superiors, over time, in the course of patient care. The operating room and the patient, however, comprise the most common, the most readily available, and often the only setting where hands-on training takes place. The novice surgeon acquires skills by first observing experienced surgeons in action and then by progressively performing, under varying degrees of supervision, more of the surgical procedures, as his/her training advances and his/her skill level increases.
A persistent-if less glamorous-challenge in hospitals lies in the day-to-day work of moving and lifting patients with impaired mobility. This is a challenge intensified by our burgeoning aging population, the obesity epidemic, and our aging healthcare workforce. During manual patient handling, the predominant risk of staff injury is excessive back and shoulder loading. A mobile robotic nurse assistant (RoNA) is highly desired to enhance the efficacy and quality of care that nurses and their paraprofessional staff can provide. Such an assistant could improve a nurse's working conditions by off-loading some of his or her most physically demanding duties, thereby reducing the potential for self-injury or injury to the patient. Hstar Technologies is developing a revolutionary RoNA system that provides physical assistance to nurses in a hospital ward. The design of RoNA is a safe and robust system that works effectively in a hospital environment under direct and telepresence control by a nurse or physician. RoNA has a humanoid design featuring bimanual dexterous manipulators that employ a series-elasticactuation (SEA) system. These electric actuators provide manipulator compliance, safety, flexibility and the strength to lift patients weighing up to 300lbs. RoNA also features an innovative humanoid upper torso, a unique mobile platform with holonomic drive and posture stability enhancement, intelligent navigation control with 3D sensing and perception capability, an intuitive and innovative human-robot interaction control interface, and a highly integrated plan for healthcare system assembly. We anticipate that robotic maneuvering assistants would increase job satisfaction, reduce lifting-related injuries, and extend the years of effective service nurses could render in hospitals. These effects would reduce hospital costs and ameliorate problems posed by the shortage of nursing staff.
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