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Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/., Comment: 10 pages. Webpage: https://airvlab.github.io/cathaction/

Augmented reality (AR) has the potential to improve the immersion and efficiency of computer-assisted orthopaedic surgery (CAOS) by allowing surgeons to maintain focus on the operating site rather than external displays in the operating theatre. Successful deployment of AR to CAOS requires a calibration that can accurately calculate the spatial relationship between real and holographic objects. Several studies attempt this calibration through manual alignment or with additional fiducial markers in the surgical scene. We propose a calibration system that offers a direct method for the calibration of AR head-mounted displays (HMDs) with CAOS systems, by using infrared-reflective marker-arrays widely used in CAOS. In our fast, user-agnostic setup, a HoloLens 2 detected the pose of marker arrays using infrared response and time-of-flight depth obtained through sensors onboard the HMD. Registration with a commercially available CAOS system was achieved when an IR marker-array was visible to both devices. Study tests found relative-tracking mean errors of 2.03 mm and 1.12{\deg} when calculating the relative pose between two static marker-arrays at short ranges. When using the calibration result to provide in-situ holographic guidance for a simulated wire-insertion task, a pre-clinical test reported mean errors of 2.07 mm and 1.54{\deg} when compared to a pre-planned trajectory., Comment: Published in conference proceedings for 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). For associated code visit: https://github.com/HL2-DINO

The radiative decay of excited states of the negative ion of rhodium, Rh$^-$, has been investigated experimentally and theoretically. The experiments were conducted at the Double ElectroStatic Ion Ring Experiment (DESIREE) facility at Stockholm University using selective photodetachment from a stored ion beam to monitor the time evolution of the excited state populations. The lifetimes of the Rh$^-$ $^3F_{3}$ and $^3F_{2}$ fine structure levels were measured to be 3.2(6)~s and 21(4)~s, respectively. An additional, previously unreported, higher-lying bound state of mixed $^1D_2+^3P_2+(4d^95s)^1D_2+^3F_2$ composition was observed and found to have a lifetime of 10.9(8)s. The binding energy of this state was determined to be in the interval $0.1584(2) $ eV $ < E_b < 0.2669(2)$ eV, using laser photodetachment threshold (LPT) spectroscopy. An autodetaching state with a lifetime of 480(10) microseconds was also observed. Theoretical calculations of the excited-state compositions, energies, and magnetic-dipole transition lifetimes were performed using the multiconfiguration Dirac-Hartree-Fock and relativistic configuration interaction methods. The calculated lifetimes of the $^3F_{3}$ and $^3F_{2}$ fine structure levels are in excellent agreement with the measured values. The present study should provide valuable insights into electron correlation effects in negative ions and forbidden radiative transitions.

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's health due to prolonged radiation exposure. As an alternative, interventional ultrasound has notable benefits such as being radiation-free, fast to deploy, and having a small footprint in the operating room. Yet, ultrasound is hard to interpret, and highly prone to artifacts and noise. Additionally, interventional radiologists must undergo extensive training before they become qualified to diagnose and treat patients effectively, leading to a shortage of staff, and a lack of open-source datasets. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary map estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future., Comment: This work has been submitted to the IEEE for possible publication

To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. Prolonged Fluoroscopic exposure is harmful for the patient and the clinician, and may lead to severe post-operative sequlae such as the development of cancer. Meanwhile, the use of interventional Ultrasound has gained popularity, due to its well-known benefits of small spatial footprint, fast data acquisition, and higher tissue contrast images. However, ultrasound images are hard to interpret, and it is difficult to localise vessels, catheters, and guidewires within them. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. In order to facilitate training of such deep learning networks, we introduce a new data synthesis pipeline that used physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon-based aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound., Comment: This work has been submitted to the IEEE for possible publication

In recent years, the steerable needles have attracted significant interest in Minimally Invasive Surgery (MIS). Amongst these, the flexible Programmable-bevel tip needle (PBN) concept has successfully achieved an in-vivo demonstration to evaluate the feasibility of Convection Enhanced Delivery (CED) of chemotherapeutics within the ovine model, with a 2.5 mm PBN prototype. However, further size reduction is necessary for other diagnostic and therapeutic procedures involving deep-seated tissue structures. Since PBNs have a complex cross-section geometry, standard production methods, such as extrusion, fails as the outer diameter is reduced further. This paper presents our first attempt to demonstrate a new manufacturing method for the PBN that employs thermal drawing technology. Experimental characterisation tests were performed for the 2.5 mm PBN and a new 1.3 mm Thermally Drawn (TD) PBN prototype described here. The results show that thermal drawing presents a significant advantage in miniaturising complex needle structures. However, the steering behaviour is affected due to the choice of material in this first attempt, a limitation which will be addressed in future work.

In this article, we investigate the model based position control of soft hydraulic actuators arranged in an antagonistic pair. A dynamical model of the system is constructed by employing the port-Hamiltonian formulation. A control algorithm is designed with an energy shaping approach which accounts for the pressure dynamics of the fluid. A nonlinear observer is included to compensate the effect of unknown external forces. Simulations demonstrate the effectiveness of the proposed approach, and experiments achieve positioning accuracy of 0.043 mm with a standard deviation of 0.033 mm in the presence of constant external forces up to 1 N., Comment: Final Version of Paper, updated figures and discussion. Accepted for IEEE International Conference on Robotics and Automation ICRA 2023

Telesurgical robotic systems provide a well established form of assistance in the operating theater, with evidence of growing uptake in recent years. Until now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale, California) has been the most widely adopted robot of this kind, with more than 6,700 systems in current clinical use worldwide [1]. To accelerate research on robotic-assisted surgery, the retired first-generation da Vinci robots have been redeployed for research use as "da Vinci Research Kits" (dVRKs), which have been distributed to research institutions around the world to support both training and research in the sector. In the past ten years, a great amount of research on the dVRK has been carried out across a vast range of research topics. During this extensive and distributed process, common technical issues have been identified that are buried deep within the dVRK research and development architecture, and were found to be common among dVRK user feedback, regardless of the breadth and disparity of research directions identified. This paper gathers and analyzes the most significant of these, with a focus on the technical constraints of the first-generation dVRK, which both existing and prospective users should be aware of before embarking onto dVRK-related research. The hope is that this review will aid users in identifying and addressing common limitations of the systems promptly, thus helping to accelerate progress in the field., Comment: 15 pages, 7 figures

Soft robotic manipulators are attractive for a range of applications such as medical interventions or industrial inspections in confined environments. A myriad of soft robotic manipulators have been proposed in the literature, but their designs tend to be relatively similar, and generally offer a relatively low force. This limits the payload they can carry and therefore their usability. A comparison of force of the different designs is not available under a common framework, and designs present different diameters and features that make them hard to compare. In this paper, we present the design of a soft robotic manipulator that is optimised to maximise its force while respecting typical application constraints such as size, workspace, payload capability, and maximum pressure. The design presented here has the advantage that it morphs to an optimal design as it is pressurised to move in different directions, and this leads to higher lateral force. The robot is designed using a set of principles and thus can be adapted to other applications. We also present a non-dimensional analysis for soft robotic manipulators, and we apply it to compare the performance of the design proposed here with other designs in the literature. We show that our design has a higher force than other designs in the same category. Experimental results confirm the higher force of our proposed design., Comment: 8 pages

Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots, such as long training duration and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization. Furthermore, most current simulators are closed-source, which hinders the collaborative development of safe and reliable autonomous systems. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms. The experimental results show that our open-source simulator can mimic the behaviour of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/robotvisionlabs/cathsim.

Conventional computer-assisted orthopaedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. Visual tracking has recently been proposed to measure the target anatomy in a markerless and effortless way, but the existing methods fail under real-world occlusion caused by intraoperative interventions. Furthermore, such methods are hardware-specific and not accurate enough for surgical applications. In this paper, we propose a RGB-D sensing-based markerless tracking method that is robust against occlusion. We design a new segmentation network that features dynamic region-of-interest prediction and robust 3D point cloud segmentation. As it is expensive to collect large-scale training data with occlusion instances, we also propose a new method to create synthetic RGB-D images for network training. Experimental results show that our proposed markerless tracking method outperforms recent state-of-the-art approaches by a large margin, especially when an occlusion exists. Furthermore, our method generalises well to new cameras and new target models, including a cadaver, without the need for network retraining. In practice, by using a high-quality commercial RGB-D camera, our proposed visual tracking method achieves an accuracy of 1-2 degress and 2-4 mm on a model knee, which meets the standard for clinical applications., Comment: This work has been submitted to the IEEE for possible publication

The inaccessibility of user-perceived reality remains an open issue in pursuing the accurate calibration of optical see-through (OST) head-mounted displays (HMDs). Manual user alignment is usually required to collect a set of virtual-to-real correspondences, so that a default or an offline display calibration can be updated to account for the user's eye position(s). Current alignment-based calibration procedures usually require point-wise alignments between rendered image point(s) and associated physical landmark(s) of a target calibration tool. As each alignment can only provide one or a few correspondences, repeated alignments are required to ensure calibration quality. This work presents an accurate and tool-less online OST calibration method to update an offline-calibrated eye-display model. The user's bare hand is markerlessly tracked by a commercial RGBD camera anchored to the OST headset to generate a user-specific cursor for correspondence collection. The required alignment is object-wise, and can provide thousands of unordered corresponding points in tracked space. The collected correspondences are registered by a proposed rotation-constrained iterative closest point (rcICP) method to optimise the viewpoint-related calibration parameters. We implemented such a method for the Microsoft HoloLens 1. The resiliency of the proposed procedure to noisy data was evaluated through simulated tests and real experiments performed with an eye-replacement camera. According to the simulation test, the rcICP registration is robust against possible user-induced rotational misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37 mm) positional error and 1.76 degree rotational error by camera-based tests in the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by user tests., Comment: accepted to ISMAR 2021

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.