This paper provides a broad overview of medical robot systems used in surgery. After introducing basic concepts of computer-integrated surgery, surgical CAD/CAM, and surgical assistants, it discusses some of the major design issues particular to medical robots. It then illustrates these issues and the broader themes introduced earlier with examples of current surgical CAD/CAM and surgical assistant systems. Finally, it provides a brief synopsis of current research challenges and closes with a few thoughts on the research/industry/clinician teamwork that is essential for progress in the field.

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Medical robotics in computer-integrated surgery

A surgical apparatus includes a surgical device, configured to be manipulated by a user to perform a procedure on a patient, and a computer system. The computer system is programmed to implement control parameters for controlling the surgical device to provide at least one of haptic guidance to the user and a limit on user manipulation of the surgical device, based on a relationship between an anatomy of the patient and at least one of a position, an orientation, a velocity, and an acceleration of a portion of the surgical device, and to adjust the control parameters in response to movement of the anatomy during the procedure.

Haptic guidance system and method

It is often desirable to define objects with respect to images of an anatomy displayed using an image guided surgery system. For non-trivial objects, or those with complicated two or three dimensional forms, it may be difficult to present information in a manner that is simple for a user to understand. The local distance to a surface of interest, such as the surface of the defined object, or to a desired position, the local penetration distance of the surface of interest, or haptic repulsion force, often provides the most useful information for augmenting the interaction of the user with the image guided surgery system. The scalar value of the local distance may be conveyed to the user by visual, audio, tactile, haptic, or other means.

Guidance system and method for surgical procedures with improved feedback

Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the reinforcement learning framework. Their advantage of being able to search for optimal policies using low-variance gradient estimates has made them useful in several real-life applications, such as robotics, power control, and finance. Although general surveys on reinforcement learning techniques already exist, no survey is specifically dedicated to actor-critic algorithms in particular. This paper, therefore, describes the state of the art of actor-critic algorithms, with a focus on methods that can work in an online setting and use function approximation in order to deal with continuous state and action spaces. After starting with a discussion on the concepts of reinforcement learning and the origins of actor-critic algorithms, this paper describes the workings of the natural gradient, which has made its way into many actor-critic algorithms over the past few years. A review of several standard and natural actor-critic algorithms is given, and the paper concludes with an overview of application areas and a discussion on open issues.

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A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

A method for controlling a surgical device is provided. The method includes manipulating the surgical device to perform a procedure on a patient; determining whether a relationship between an anatomy of the patient and a position, an orientation, a velocity, and/or an acceleration of a surgical tool of the surgical device corresponds to a desired relationship between the anatomy and the position, the orientation, the velocity, and/or the acceleration of the surgical tool; and imposing a constraint on the surgical device if the relationship does not correspond to the desired relationship and/or a detection device is unable to detect a position of the anatomy and/or the position of the surgical tool.

Method and apparatus for controlling a haptic device

We are developing virtual fixtures for the internal mammary artery (IMA) harvest portion of robot-assisted coronary artery bypass graft procedures. A preoperative CT scan will be processed to define the location of the IMA. In surgery, the patient's anatomy will be registered to the image data, then a virtual fixture will constrain the instrument's motions, as commanded by the surgeon, to appropriate paths adjacent to the artery. As a preliminary test, a virtual wall is implemented on a commercial surgical robot system. Results from a dissection task show that execution is faster and more precise than with conventional freehand techniques.

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Virtual Fixtures for Robotic Cardiac Surgery

Nonpharmacologic and nonsurgical transcranial modulation of the nerve function may provide new opportunities in evaluation and treatment of cranial nerve diseases. This study investigates the possibility of using low-intensity transcranial focused ultrasound (FUS) to selectively stimulate the rat abducens nerve located above the base of the skull. FUS (frequencies of 350 kHz and 650 kHz) operating in a pulsed mode was applied to the abducens nerve of Sprague-Dawley rats under stereotactic guidance. The abductive eyeball movement ipsilateral to the side of sonication was observed at 350 kHz, using the 0.36-msec tone burst duration (TBD), 1.5-kHz pulse repetition frequency (PRF), and the overall sonication duration of 200 msec. Histologic and behavioral monitoring showed no signs of disruption in the blood brain barrier (BBB), as well as no damage to the nerves and adjacent brain tissue resulting from the sonication. As a novel functional neuro-modulatory modality, the pulsed application of FUS has potential for diagnostic and therapeutic applications in diseases of the peripheral nervous system.

Noninvasive Transcranial Stimulation of Rat Abducens Nerve by Focused Ultrasound

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

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Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Pulsed application of focused ultrasound (FUS) to the regional brain tissue alters the state of tissue excitability, and thus provides the means for non-invasive functional neuromodulation. We report that the application of transcranial FUS to the thalamus of anesthetized rats reduced the time to emergence of voluntary movement from intraperitoneal ketamine-xylazine anesthesia. Low intensity FUS was applied to the thalamus of anesthetized animals. The times required for the animals to show distinct physiological/behavioral changes were measured and compared to those times required in a control session without sonication. The sonication significantly reduced the time to show pinch response and voluntary movement. The modulatory effects of FUS on anesthesia suggest potential therapeutic applications for disorders of consciousness such as minimally consciousness states.

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Transcranial focused ultrasound to the thalamus alters anesthesia time in rats.

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

Shinsuk Park

Papers

Virtual Fixtures for Robotic Cardiac Surgery

Noninvasive Transcranial Stimulation of Rat Abducens Nerve by Focused Ultrasound

Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound to the thalamus alters anesthesia time in rats.

Impedance Learning for Robotic Contact Tasks Using Natural Actor-Critic Algorithm