Cable-driven parallel mechanisms for minimally invasive robotic surgery

Minimally invasive surgery (MIS) has revolutionised surgery by providing faster recovery times, less post-operative complications, improved cosmesis and reduced pain for the patient. Surgical robotics are used to further decrease the invasiveness of procedures, by using yet smaller and fewer incisions or using natural orifices as entry point. However, many robotic systems still suffer from technical challenges such as sufficient instrument dexterity and payloads, leading to limited adoption in clinical practice. Cable-driven parallel mechanisms (CDPMs) have unique properties, which can be used to overcome existing challenges in surgical robotics. These beneficial properties include high end-effector payloads, efficient force transmission and a large configurable instrument workspace. However, the use of CDPMs in MIS is largely unexplored. This research presents the first structured exploration of CDPMs for MIS and demonstrates the potential of this type of mechanism through the development of multiple prototypes: the ESD CYCLOPS, CDAQS, SIMPLE, neuroCYCLOPS and microCYCLOPS. One key challenge for MIS is the access method used to introduce CDPMs into the body. Three different access methods are presented by the prototypes. By focusing on the minimally invasive access method in which CDPMs are introduced into the body, the thesis provides a framework, which can be used by researchers, engineers and clinicians to identify future opportunities of CDPMs in MIS. Additionally, through user studies and pre-clinical studies, these prototypes demonstrate that this type of mechanism has several key advantages for surgical applications in which haptic feedback, safe automation or a high payload are required. These advantages, combined with the different access methods, demonstrate that CDPMs can have a key role in the advancement of MIS technology.Open Acces

AUGMENTED REALITY AND INTRAOPERATIVE C-ARM CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED ROBOTIC SURGERY

Minimally-invasive robotic-assisted surgery is a rapidly-growing alternative to traditionally open and laparoscopic procedures; nevertheless, challenges remain. Standard of care derives surgical strategies from preoperative volumetric data (i.e., computed tomography (CT) and magnetic resonance (MR) images) that benefit from the ability of multiple modalities to delineate different anatomical boundaries. However, preoperative images may not reflect a possibly highly deformed perioperative setup or intraoperative deformation. Additionally, in current clinical practice, the correspondence of preoperative plans to the surgical scene is conducted as a mental exercise; thus, the accuracy of this practice is highly dependent on the surgeon’s experience and therefore subject to inconsistencies. In order to address these fundamental limitations in minimally-invasive robotic surgery, this dissertation combines a high-end robotic C-arm imaging system and a modern robotic surgical platform as an integrated intraoperative image-guided system. We performed deformable registration of preoperative plans to a perioperative cone-beam computed tomography (CBCT), acquired after the patient is positioned for intervention. From the registered surgical plans, we overlaid critical information onto the primary intraoperative visual source, the robotic endoscope, by using augmented reality. Guidance afforded by this system not only uses augmented reality to fuse virtual medical information, but also provides tool localization and other dynamic intraoperative updated behavior in order to present enhanced depth feedback and information to the surgeon. These techniques in guided robotic surgery required a streamlined approach to creating intuitive and effective human-machine interferences, especially in visualization. Our software design principles create an inherently information-driven modular architecture incorporating robotics and intraoperative imaging through augmented reality. The system's performance is evaluated using phantoms and preclinical in-vivo experiments for multiple applications, including transoral robotic surgery, robot-assisted thoracic interventions, and cocheostomy for cochlear implantation. The resulting functionality, proposed architecture, and implemented methodologies can be further generalized to other C-arm-based image guidance for additional extensions in robotic surgery

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Artificial Intelligence in Surgery, Surgical Subspecialties, and Related Disciplines

Artificial intelligence (AI) and machine learning (ML) algorithms show promise in revolutionizing many aspects of surgical care. ML algorithms may be used to improve radiologic diagnosis of disease and predict peri-, intra-, and postoperative complications in patients based on their vital signs and other clinical characteristics. Computer vision may improve laparoscopic and minimally invasive surgical education by identifying and tracking the surgeon’s movements and providing real-time performance feedback. Eventually, AI and ML may be used to perform operative interventions that were not previously possible (nanosurgery or endoluminal surgery) with the utilization of fully autonomous surgical robots. Overall, AI will impact every surgical subspecialty, and surgeons must be prepared to facilitate the use of this technology to optimize patient care. This chapter will review the applications of AI across different surgical disciplines, the risks and limitations associated with AI and ML, and the role surgeons will play in implementing this technology into their practice

Clinical Considerations for Flexible Access Surgery

The expectation of excellence in health care in modern times continues to be challenged. Government and patients alike continue to demand superior health care with excellent treatment outcomes at minimal expense to their time and convenience. Although surgery is the most definitive treatment option in modern medicine, it can be the most demanding both physically and psychologically. The less invasive the procedure offered the more acceptable it has been shown to be to the patient more often with fewer complications attributed and a faster return to health (1). The positive impact of the minimally invasive concept on the healthcare system has been unfathomable. The domino effect created by the early results from laparoscopic surgery was felt not only across the surgical community but also the medical. Across different specialties, alternative novel therapeutic techniques were devised to overcome problems relating to the large operative procedures which struggled to cross over to the laparoscopic approach. The best example of this is in cardiovascular surgery, where image guided endovascular techniques have overcome the need for many of the once extensive operative procedures including the abdominal aortic aneurysm repair and the coronary bypass procedure. The risks and complications from these operative interventions remain significant and are still performed, though far less frequently than in the past. Selective aneurysms as well as primary coronary events are managed routinely through the endovascular technique with surgery being retained for the complex cases or the non-responders. It seems obvious in hindsight that given the choice of a small 5mm groin incision over a large 30cm open chest or abdominal incision which the public would choose, even with the greater long term benefits sometimes favoring the open approaches. Gastrointestinal endoscopy has the potential to move in the same direction. The use of the endoscope as a surgical tool rather than simply an investigative device has only recently been recognized, promoted through the concept of Natural Orifice Translumenal Endoscopic Surgery (NOTES). The technique aims to provide a cosmetic enhancement to routine surgical procedures by creating the access incision within a natural orifice. The endoscope provides the vision and the biopsy channels in-built are able to guide operative instruments to the target site to enable a therapeutic procedure to be undertaken. However, it would be naïve to believe that in the current state NOTES is anything but a fashionable research technique and far from routine clinical use. However, it’s most superior element, which has the potential to extend the boundaries of surgery aside from all else, is the flexibility of the platform. This thesis provides a detailed investigation into the use of the flexible endoscope as a surgical platform. It defines Flexible Access Surgery (FAS) as an all encompassing surgical technique which utilizes flexible platforms at its heart, describes some novel applications representative as examplars of the technique and explores the significant challenges which would hinder clinical translation. These challenges are described and integrated into two novel enhanced mechatronic flexible access surgical platforms which are further validated and trialed within the pre-clinical in-vivo setting as the future of flexible surgery. The major original contributions of this thesis include the description and definition of the flexible access technique with novel clinical applications. The design, construction and validation of a flexible access box simulator for describing flexible endoscopic navigation within a spatial environment highlighting the challenge this encompasses for many clinicians. The instrumental requirements are explored through the evaluation of the force requirements within the preclinical setting and the instrument refinement both in design and practice that can be adopted to optimize the force delivery particularly when relating to novel flexible platform designs. Finally, the thesis describes the integrated clinical design and validation of two enhanced mechatronic flexible access platforms and describes their clinically driven construction through a series of pre-clinical live in-vivo trials. The evolution of each device is described with performance evaluation and clinical exemplars undertaken. The impact of the results presented within this thesis and the potential for further high impact research is centered on the design and integration of future flexible robotic platforms for minimally invasive surgery. The clinical and mechanical requirements essential for optimal clinical performance will enable designs to be more clinically relevant and ultimately more clinically translatable in the future. Defining these requirements has entailed the use of mapping and sensing the relevant tools which has in turn exposed future potential research avenues to be opened into the perhaps more relevant real time evaluation of the surgical workflow, enabling clinical skills to be more reliably quantified during laparoscopic and endoscopic procedures

Automatic registration of 3D models to laparoscopic video images for guidance during liver surgery

Laparoscopic liver interventions offer significant advantages over open surgery, such as less pain and trauma, and shorter recovery time for the patient. However, they also bring challenges for the surgeons such as the lack of tactile feedback, limited field of view and occluded anatomy. Augmented reality (AR) can potentially help during laparoscopic liver interventions by displaying sub-surface structures (such as tumours or vasculature). The initial registration between the 3D model extracted from the CT scan and the laparoscopic video feed is essential for an AR system which should be efficient, robust, intuitive to use and with minimal disruption to the surgical procedure. Several challenges of registration methods in laparoscopic interventions include the deformation of the liver due to gas insufflation in the abdomen, partial visibility of the organ and lack of prominent geometrical or texture-wise landmarks. These challenges are discussed in detail and an overview of the state of the art is provided. This research project aims to provide the tools to move towards a completely automatic registration. Firstly, the importance of pre-operative planning is discussed along with the characteristics of the liver that can be used in order to constrain a registration method. Secondly, maximising the amount of information obtained before the surgery, a semi-automatic surface based method is proposed to recover the initial rigid registration irrespective of the position of the shapes. Finally, a fully automatic 3D-2D rigid global registration is proposed which estimates a global alignment of the pre-operative 3D model using a single intra-operative image. Moving towards incorporating the different liver contours can help constrain the registration, especially for partial surfaces. Having a robust, efficient AR system which requires no manual interaction from the surgeon will aid in the translation of such approaches to the clinics

Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring

Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physician’s perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by “imitating” the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the technique’s full potential. The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible. To overcome these hurdles and enable functional laparoscopy, for the first time in this field physical models are combined with powerful machine learning techniques. The physical model is employed to create highly accurate simulations, which in turn teach the algorithm to rapidly relate multispectral pixels to underlying functional changes. To reduce the domain shift introduced by learning from simulations, a novel transfer learning approach automatically adapts generic simulations to match almost arbitrary recordings of visceral tissue. In combination with the only available video-rate capable multispectral sensor, the method pioneers fluent perfusion monitoring with MSI. This system was carefully tested in a multistage process, involving in silico quantitative evaluations, tissue phantoms and a porcine study. Clinical applicability was ensured through in-patient recordings in the context of partial nephrectomy; in these, the novel system characterized ischemia live during the intervention. Verified against a fluorescence reference, the results indicate that fluent, non-invasive ischemia detection and monitoring is now possible. In conclusion, this thesis presents the first multispectral laparoscope capable of videorate functional analysis. The system was successfully evaluated in in-patient trials, and future work should be directed towards evaluation of the system in a larger study. Due to the broad applicability and the large potential clinical benefit of the presented functional estimation approach, I am confident the descendants of this system are an integral part of the next generation OR

Intraoperative fluorescence imaging : clinical translation of targeted and non-targeted tracers

Surgery is the cornerstone of curative treatment of many malignancies. However, incomplete resections and avoidable iatrogenic damage during surgery increase morbidity and mortality rates in patients. Although advances in preoperative imaging modalities have improved adequate patient selection and surgical planning, during procedures surgeons rely mainly on inspection and palpation. It is often very difficult to distinguish between fibrotic, inflamed, or malignant tissues [1]. Inspection and palpation are highly subjective and have low sensitivity for detecting cancer, especially for subcentimeter lesions [2].Near-infrared fluorescence (NIRF) imaging is a technique that enhances contrast of certain structures during surgery and thereby improves their detectability [3, 4]. It uses targeted and non-targeted fluorescent tracers in combination with dedicated NIRF imaging systems. These tracers consist of fluorophores; molecules that emit fluorescence with a certain wavelength upon excitation by an external light source. These fluorescence signals can be captured by an imaging system optimized for that specific wavelength. Especially near-infrared wavelengths (i.e. 700-900 nm) have excellent characteristics, including relatively high tissue penetration capacity and low tissue autofluorescence, and are therefore preferably used for clinical applications [5]. NIRF imaging can identify targets covered by up to 10 mm tissue.Non-targeted fluorescent tracers such as indocyanine green (ICG; emission peak 830 nm) and methylene blue (emission peak 700 nm) have been available for several decades, albeit for different indications. Their off-label use is safe and cheap, which contributed significantly to clinical experience and enabled NIRF imaging research to get momentum (chapter 2 and 3). NIRF imaging systems could be developed simultaneously with improved fluorophores. In general, NIRF-guided surgery has the potential to increase radical resection rates, while reducing avoidable iatrogenic damage. Both non-targeted as well as targeted tracers will be discussed, followed by the future perspectives of NIRF imaging.Non-specificLUMC / Geneeskund

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions