Therapies targeting neurodegenerative diseases via brain ventricles and spinal parenchyma face delivery challenges. Systemic administration is ineffective due to the blood-brain barrier, while direct surgical access, especially for multi-site delivery, is highly invasive. The spinal subarachnoid space offers potential for microcatheter-based delivery, but existing robotic catheter technologies are unsuitable due to spinal anatomy constraints. This paper presents a miniaturised and sensorised steerable eversion-growing robot tailored to navigation of the subarachnoid space of the spine. The property of eversion reduces interaction forces with the anatomy, rendering our approach safer than microcatheters that need to be pushed. Our system is capable of real-time tip force estimation with three degrees of freedom (DoF) using fibre Bragg gratings (FBG). Additionally, it incorporates a micro-endoscope and a steerable tip, all within a tiny 2mm outer diameter. The system’s navigation, sensing, and imaging capabilities were evaluated using a realistic up-scaled phantom of the subarachnoid space covering the cervical spine, demonstrating interaction forces within the safe range of 2-5N during phantom navigation. Comparison study of instrument-tissue interactions further approved its clinical relevance, presenting a 73.78% decrease of the mean absolute forces to traditional insertion without the sheath in global measurements.

Tumors affecting abdominal organs rank among the deadliest malignancies. In this context, Magnetic Resonance Imaging (MRI) serves as an effective diagnostic tool with a strong potential to support image-guided minimally invasive interventions for treating these tumours, offering an ionizing-radiation-free medical modality. MRI provides exceptional soft tissue contrast and multi-angle imaging, enabling accurate intraoperative localisation of target tumours within these vital organs. Nevertheless, MRI-guided minimally invasive interventions still encounter significant challenges due to the strong magnetic field environment and the narrow and deep bore of MRI machines. This paper proposes a novel MR safe 5-degrees-of-freedom (DoFs) parallel table-mounted double-arch needle insertion robot with a scissor-folding mechanism (SFM) for abdominal interventions. The proposed robot is designed to fit a standard 70-cm MRI bore. Initial evaluation experiments indicate mean errors of 3.14 mm for the proposed robotic arch and 2.23 mm for the full needle insertion robot, respectively. Additionally, preliminary testing of the system in an MRI environment resulted in unaltered MRI imaging output, with negligible artefacts associated with the presence of the robot within the bore.

Cochlear implants (CIs) can improve hearing in patients suffering from sensorineural hearing loss via an electrode array (EA) carefully inserted in the scala tympani. Current EAs can cause trauma during insertion, threatening hearing preservation; hence we proposed a pre-curved thermally drawn EA that curls into the cochlea under the influence of body temperature. However, the additional surgical skill required to insert pre-curved EAs usually produces worse surgical outcomes. Medical robots can offer an effective solution to assist surgeons in improving surgical outcomes and reducing outliers. This work proposes a collaborative approach to insert our EA where manageable tasks are automated using a vision-based system. The insertion strategy presented allowed us to insert our EA successfully. The feasibility study showed that we can insert EAs following the defined control strategy while keeping the exerted contact forces within safe levels. The teleoperated robotic system and robotic vision approach to control a self-shaping CI has thus shown potential to provide the tools for a more delicate and atraumatic approach.

Master-slave systems for endovascular catheterization have brought major clinical benefits including reduced radiation doses to the operators, improved precision and stability of the instruments, as well as reduced procedural duration. Emerging deep reinforcement learning (RL) technologies could potentially automate more complex endovascular tasks with enhanced success rates, more consistent motion and reduced fatigue and cognitive workload of the operators. However, the complexity of the pulsatile flows within the vasculature and non-linear behavior of the instruments hinder the use of model-based approaches for RL. This paper describes model-free generative adversarial imitation learning to automate a standard arterial catherization task. The automation policies have been trained in a pre-clinical setting. Detailed validation results show high success rates after skill transfer to a different vascular anatomical model. The quality of the catheter motions also shows less mean and maximum contact forces compared to manual-based approaches.

Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on smallscale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermore, the temporal continuity in intraoperative imaging sequences is not fully utilised. In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. The proposed FW-Net has three modules: a segmentation network with encoder-decoder architecture, a flow network to extract optical flow information, and a novel flow-guided warping function to learn the frame-to-frame temporal continuity. We show that by effectively learning temporal continuity, the network can successfully segment and track the catheters in real-time sequences using only raw ground-truth for training. Detailed validation results confirm that our FW-Net outperforms stateof-the-art techniques while achieving real-time performance.

Cardiovascular diseases remain as the most common cause of death worldwide. Remotely manipulated robotic systems are utilized to perform minimally invasive endovascular interventions. The main benefits of this methodology include reduced recovery time, improvement of clinical skills and procedural facilitation. Currently, robotic assistance, precision, and stability of instrument manipulation are compensated by the lack of haptic feedback and an excessive amount of radiation to the patient. This paper proposes a novel master-slave robotic platform that aims to bring the haptic feedback benefit on the master side, providing an intuitive user interface, and clinical familiar workflow. The slave robot is capable of manipulating conventional catheters and guidewires in multi-modal imaging environments. The system has been initially tested in a phantom cannulation study under fluoroscopic guidance, evaluating its reliability and procedural protocol. As the slave robot has been entirely produced by additive manufacturing and using pneumatic actuation, MR compatibility is enabled and was evaluated in a preliminary study. Results of both studies strongly support the applicability of the robot in different imaging environments and prospective clinical translation.

Vascular diseases are the most common precursors to ischemic heart disease and stroke, which are two of the leading causes of death worldwide. Endovascular intervention is a minimally invasive surgical approach to treat such diseases. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anesthesia, reduced blood loss and significantly lower mortality. Endovascular procedures require high surgical skills to minimize contacts between the manipulated instruments (catheters and guidewires) and the vessel wall, which represent one of the major risks for the patient. Robotic assistance can potentially improve the precision and stability of instruments manipulation. One key limitation of current commercial robotic platforms is the lack of haptic feedback, preventing their acceptance and limiting the clinical usability. This paper proposes to bring the benefit of haptic feedback to robot-assisted endovascular intervention. Here we hypothesize that the introduction of 3D haptic guidance during robot-assisted endovascular procedure can further improve the surgical performance and safety while overcoming the limitations of currently available technology. The proposed 3D haptic guidance allows the surgeon to sense the vasculature while controlling a catheter through a robotic haptic manipulator. Validation of the system is performed through end-user experiments with vascular surgeons on a bespoke surgical simulator. The obtained results demonstrate that 3D haptic guidance has the potential of improving effectiveness, precision, and safety of endovascular intervention. Furthermore, vascular surgeons found the proposed technology safe and overall easy to use, indicating its potential on real surgical procedures.

Bronchoscopic intervention, as a minimally invasive method for the diagnosis and treatment of lung diseases, has attracted more and more attention in recent years. However, existing endobronchial instruments lack the steerability accessing the peripheral airways with difficult bifurcations. This paper presents a novel wire-driven dexterous manipulator for the guidance of such instruments. Precision laser profiling is used to cut a stainless steel tube into multiple interlocked segments with revolute joints. The outer diameter of the manipulator is 2.20 mm which is small enough to be inserted into the working channels of most commercial bronchoscopes and distal airways, while keeping a large inner lumen with a diameter of 1.44 mm for passing various bronchoscopic instruments. The small bending radius provides enough flexibility to navigate inside the complex bronchial tree. Two kinematic models are proposed to predict the manipulator configuration from the translation of actuation wires. The former model is geometrically derived with the assumption of constant curvature bending and the latter one is statistically driven by capturing the motion trajectories of manipulator joints. A prototype of our low-cost add-on instrument guidance robot for bronchoscopic intervention is presented which can be easily integrated into current clinical routine.

Emerging robot-assisted endovascular intervention has the potential to reduce X-ray radiations to the operator while enhancing the stability and dexterity of catheter manipulation. Supervised and shared autonomy of endovascular procedures could add further improvements in reduced fatigue and cognitive workloads of the operator, higher success rates of cannulation and improved surgical outcomes. However, robotic path planning for endovascular procedure is challenging due to complex and non-linear flow dynamics inside the vasculature. This paper presents a learning-based robotic catheterization platform addressing those challenges, this approach incorporates path integral reinforcement learning (RL) framework based on dynamic movement primitives (DMP) to enhance catheterization tasks by a customized robotic manipulator. The robotic trajectories were optimized through RL in order to avoid unwanted contacts between the catheter tip and the vessel wall. The proposed methods can adapt to different flow simulations, vascular models, and catheterization tasks. The quality of the catheterization was evaluated with performance metrics. The results show significant refinement of catheter paths by the proposed approach, resulting in shorter overall lengths and fewer contact forces, which can potentially reduce risks in endothelial wall damages, embolization, and stroke. The results support the development of robotic path planning for endovascular procedures as well as designing intelligent, hands-on robotic navigation platforms.

Robotic and computer assistance can bring significant benefits to endovascular procedures in terms of precision and stability, reduced radiation doses, improved comfort and access to difficult and tortuous anatomy. However, the design of current commercially available platforms tends to alter the natural bedside manipulation skills of the operator, so that the manually acquired experience and dexterity are not well utilized. Furthermore, most of these systems lack of haptic feedback, preventing their acceptance and limiting the clinical usability. In this paper a new robotic platform for endovascular catheterization, the CathBot, is presented. It is an ergonomic master-slave system with navigation system and integrated vision-based haptic feedback, designed to maintain the natural bedside skills of the vascular surgeon. Unlike previous work reported in literature, dynamic motion tracking of both the vessel walls the catheter tip is incorporated to create dynamic active constraints. The system was evaluated through a combined quantitative and qualitative user study simulating catheterization tasks on a phantom. Forces exerted on the phantom were measured. The results showed a 70% decrease in mean force and 61% decrease in maximum force when force feedback is provided. This research provides the first integration of vision-based dynamic active constraints within an ergonomic robotic catheter manipulator. The technological advances presented here, demonstrates that vision-based haptic feedback can improve the effectiveness, precision, and safety of robot-assisted endovascular procedures.

Breast cancer is the most frequently life-threatening diagnosed type of cancer among women. Early and accurate diagnosis by acquiring a tissue sample using biopsy techniques is essential. However, small lesions only visible by MRI are often missed in standard methods, indicating the need for a robotic-assisted biopsy system that is MRI-compatible. Existing proof-of-concepts are difficult to employ due to large sizes and/or actuation complexities. Therefore, a compact pneumatically-actuated 5 DOF MRI-compatible robot was further developed and controlled by a computerized valve manifold. Accuracy and efficiency measurements have been performed using two different PVC breast phantoms with embedded fish oil capsules (mimicking lesions) inside a 0.25T MRI scanner. Preliminary results show that the end-effector was able to hit the targeted capsules, and that the position accuracy is in the range of 4.7-7.3 mm. The developed robotic system has potential to perform MRI-guided breast biopsies accurately and improve the clinical workflow.