Accurate multi-robot targeting for keyhole neurosurgery based on external sensors monitoring

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy

Convergence Analysis of an Iterative Targeting Method for Keyhole Robotic Surgery

In surgical procedures, robots can accurately position and orient surgical instruments. Intraoperatively, external sensors can localize the instrument and compute the targeting movement of the robot, based on the transformation between the coordinate frame of the robot and the sensor. This paper addresses the assessment of the robustness of an iterative targeting algorithm in perturbed conditions. Numerical simulations and experiments (with a robot with seven degrees of freedom and an optical tracking system) were performed for computing the maximum error of the rotational part of the calibration matrix, which allows for convergence, as well as the number of required iterations. The algorithm converges up to 50 degrees of error within a large working space. The study confirms the clinical relevance of the method because it can be applied on commercially available robots without modifying the internal controller, thus improving the targeting accuracy and meeting surgical accuracy requirements

Review of robotic technology for keyhole transcranial stereotactic neurosurgery

The research of stereotactic apparatus to guide surgical devices began in 1908, yet a major part of today's stereotactic neurosurgeries still rely on stereotactic frames developed almost half a century ago. Robots excel at handling spatial information, and are, thus, obvious candidates in the guidance of instrumentation along precisely planned trajectories. In this review, we introduce the concept of stereotaxy and describe a standard stereotactic neurosurgery. Neurosurgeons' expectations and demands regarding the role of robots as assistive tools are also addressed. We list the most successful robotic systems developed specifically for or capable of executing stereotactic neurosurgery. A critical review is presented for each robotic system, emphasizing the differences between them and detailing positive features and drawbacks. An analysis of the listed robotic system features is also undertaken, in the context of robotic application in stereotactic neurosurgery. Finally, we discuss the current perspective, and future directions of a robotic technology in this field. All robotic systems follow a very similar and structured workflow despite the technical differences that set them apart. No system unequivocally stands out as an absolute best. The trend of technological progress is pointing toward the development of miniaturized cost-effective solutions with more intuitive interfaces.This work has been partially financed by the NETT Project (FP7-PEOPLE-2011-ITN-289146), ACTIVE Project (FP7-ICT-2009-6-270460), and FCT PhD grant (ref. SFRH/BD/86499/2012)

Ultrasound-Augmented Laparoscopy

Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed. To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space

From Concept to Market: Surgical Robot Development

Surgical robotics and supporting technologies have really become a prime example of modern applied information technology infiltrating our everyday lives. The development of these systems spans across four decades, and only the last few years brought the market value and saw the rising customer base imagined already by the early developers. This chapter guides through the historical development of the most important systems, and provide references and lessons learnt for current engineers facing similar challenges. A special emphasis is put on system validation, assessment and clearance, as the most commonly cited barrier hindering the wider deployment of a system

Robotic Handle Prototypes for Endoscopic Endonasal Skull Base Surgery: Pre-clinical Randomised Controlled Trial of Performance and Ergonomics

Endoscopic endonasal skull base surgery is a promising alternative to transcranial approaches. However, standard instruments lack articulation, and thus, could benefit from robotic technologies. The aim of this study was to develop an ergonomic handle for a handheld robotic instrument intended to enhance this procedure. Two different prototypes were developed based on ergonomic guidelines within the literature. The first is a forearm-mounted handle that maps the surgeon’s wrist degrees-of-freedom to that of the robotic end-effector; the second is a joystick-and-trigger handle with a rotating body that places the joystick to the position most comfortable for the surgeon. These handles were incorporated into a custom-designed surgical virtual simulator and were assessed for their performance and ergonomics when compared with a standard neurosurgical grasper. The virtual task was performed by nine novices with all three devices as part of a randomised crossover user-study. Their performance and ergonomics were evaluated both subjectively by themselves and objectively by a validated observational checklist. Both handles outperformed the standard instrument with the rotating joystick-body handle offering the most substantial improvement in terms of balance between performance and ergonomics. Thus, it is deemed the more suitable device to drive instrumentation for endoscopic endonasal skull base surgery

Sistema no invasivo para la medida y visualización de desplazamientos de tejidos en neurocirugía

[ES] Se presenta un sistema de imagen médica basado en medición de distancias entre tejidos por medio de microondas. El sistema está pensado para la monitorización y corrección del problema de brain-shift en operaciones de tumores cerebrales. Se muestra el sistema desarrollado y los primeros resultados con phantoms de tejidos cerebrales que tienen una morfología similar a los tejidos reales. Se demuestra la viabilidad del sistema para la medida de distancias y la reconstrucción de una imagen 3D intraoperatoria.[EN] A medical imaging system based on measuring distances between tissues by means of microwaves is presented. The system is designed for the monitoring and correction of the brain-shift problem in brain tumor operations. It shows the developed system and the first results with phantoms of brain tissues that have a morphology similar to real tissues. The viability of the system for the measurement of distances and the reconstruction of an intraoperative 3D image is demonstrated.Juan, C.; Blanco, C.; Herrero, N.; Garcia, H.; Vicente-Samper, J.; Avila, E.; Sabater-Navarro, J. (2019). Sistema no invasivo para la medida y visualización de desplazamientos de tejidos en neurocirugía. En 11º Simposio CEA de Bioingeniería. Editorial Universitat Politècnica de València. 76-84. https://doi.org/10.4995/CEABioIng.2019.10033OCS768

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System