Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Deep tissue monitoring enabled by wearable ultrasonic devices and machine learning

Benefiting from the development of wearable electronic devices, various physiological signals, such as body temperature, hydration, glucose/lactate levels, and local field potentials, can already be monitored continuously and noninvasively. Among all physiological signals, those deeply beneath the skin, including central blood pressure, blood flow and activities of major organs, are particularly important since they are directly related to the subject’s life-sustaining capability. However, there is a lack of devices that could give continuous and reliable readings of these vital signs. Their common limitations can be summarized as: limited penetration depth and operator dependence. Herein, we use human carotid artery as an example and demonstrate wearable ultrasonic devices supported by control electronics and adaptive algorithms to achieve automatic deep tissue monitoring. To eliminate the operator dependence of ultrasound technology, machine learning-based algorithms were developed addressing the blood vessel positioning and wall tracking tasks

Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives

This article reviews the recent advances in intelligent robotic ultrasound (US) imaging systems. We commence by presenting the commonly employed robotic mechanisms and control techniques in robotic US imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. Moreover, we conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area.Comment: Accepted by Annual Review of Control, Robotics, and Autonomous System

Respiratory-induced organ motion compensation for MRgHIFU

Summary: High Intensity Focused Ultrasound is an emerging non-invasive technology for the precise thermal ablation of pathological tissue deep within the body. The fitful, respiratoryinduced motion of abdominal organs, such as of the liver, renders targeting challenging. The work in hand describes methods for imaging, modelling and managing respiratoryinduced organ motion. The main objective is to enable 3D motion prediction of liver tumours for the treatment with Magnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU). To model and predict respiratory motion, the liver motion is initially observed in 3D space. Fast acquired 2D magnetic resonance images are retrospectively reconstructed to time-resolved volumes, thus called 4DMRI (3D + time). From these volumes, dense deformation fields describing the motion from time-step to time-step are extracted using an intensity-based non-rigid registration algorithm. 4DMRI sequences of 20 subjects, providing long-term recordings of the variability in liver motion under free breathing, serve as the basis for this study. Based on the obtained motion data, three main types of models were investigated and evaluated in clinically relevant scenarios. In particular, subject-specific motion models, inter-subject population-based motion models and the combination of both are compared in comprehensive studies. The analysis of the prediction experiments showed that statistical models based on Principal Component Analysis are well suited to describe the motion of a single subject as well as of a population of different and unobserved subjects. In order to enable target prediction, the respiratory state of the respective organ was tracked in near-real-time and a temporal prediction of its future position is estimated. The time span provided by the prediction is used to calculate the new target position and to readjust the treatment focus. In addition, novel methods for faster acquisition of subject-specific 3D data based on a manifold learner are presented and compared to the state-of-the art 4DMRI method. The developed methods provide motion compensation techniques for the non-invasive and radiation-free treatment of pathological tissue in moving abdominal organs for MRgHIFU. ---------- Zusammenfassung: High Intensity Focused Ultrasound ist eine aufkommende, nicht-invasive Technologie für die präzise thermische Zerstörung von pathologischem Gewebe im Körper. Die unregelmässige ateminduzierte Bewegung der Unterleibsorgane, wie z.B. im Fall der Leber, macht genaues Zielen anspruchsvoll. Die vorliegende Arbeit beschreibt Verfahren zur Bildgebung, Modellierung und zur Regelung ateminduzierter Organbewegung. Das Hauptziel besteht darin, 3D Zielvorhersagen für die Behandlung von Lebertumoren mittels Magnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) zu ermöglichen. Um die Atembewegung modellieren und vorhersagen zu können, wird die Bewegung der Leber zuerst im dreidimensionalen Raum beobachtet. Schnell aufgenommene 2DMagnetresonanz- Bilder wurden dabei rückwirkend zu Volumen mit sowohl guter zeitlicher als auch räumlicher Auflösung, daher 4DMRI (3D + Zeit) genannt, rekonstruiert. Aus diesen Volumen werden Deformationsfelder, welche die Bewegung von Zeitschritt zu Zeitschritt beschreiben, mit einem intensitätsbasierten, nicht-starren Registrierungsalgorithmus extrahiert. 4DMRI-Sequenzen von 20 Probanden, welche Langzeitaufzeichungen von der Variabilität der Leberbewegung beinhalten, dienen als Grundlage für diese Studie. Basierend auf den gewonnenen Bewegungsdaten wurden drei Arten von Modellen in klinisch relevanten Szenarien untersucht und evaluiert. Personen-spezifische Bewegungsmodelle, populationsbasierende Bewegungsmodelle und die Kombination beider wurden in umfassenden Studien verglichen. Die Analyse der Vorhersage-Experimente zeigte, dass statistische Modelle basierend auf Hauptkomponentenanalyse gut geeignet sind, um die Bewegung einer einzelnen Person sowie einer Population von unterschiedlichen und unbeobachteten Personen zu beschreiben. Die Bewegungsvorhersage basiert auf der Abschätzung der Organposition, welche fast in Echtzeit verfolgt wird. Die durch die Vorhersage bereitgestellte Zeitspanne wird verwendet, um die neue Zielposition zu berechnen und den Behandlungsfokus auszurichten. Darüber hinaus werden neue Methoden zur schnelleren Erfassung patienten-spezifischer 3D-Daten und deren Rekonstruktion vorgestellt und mit der gängigen 4DMRI-Methode verglichen. Die entwickelten Methoden beschreiben Techniken zur nichtinvasiven und strahlungsfreien Behandlung von krankhaftem Gewebe in bewegten Unterleibsorganen mittels MRgHIFU

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi