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.

The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for the need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation exposure. In this paper, a skeleton instantiation framework of Abdominal Aortic Aneurysm (AAA) from a single 2D fluoroscopic image is introduced for real-time 3D robotic path planning. A graph matching method is proposed to establish the correspondences between the 3D preoperative and 2D intra-operative AAA skeletons, and then the two skeletons are registered by skeleton deformation and regularization in respect to skeleton length and smoothness. Furthermore, deep learning was used to segment 3D preoperative AAA from Computed Tomography (CT) scans to facilitate the framework automation. Simulation, phantom and patient AAA data sets have been used to validate the proposed framework. 3D distance error of 2mm was achieved in the phantom setup. Performance advantages were also achieved in terms of accuracy, robustness and time-efficiency.

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.

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.

Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently navigated by 2D fluoroscopy which is insufficiently informative. Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a single fluoroscopy projection in real-time, which could help 3D FEVAR navigation and robotic path planning. This proposed semi-automatic method was based on the Robust Perspective-S-Point (RP5P) method, graft gap interpolation and semiautomatic multiple-class marker center determination. In this paper, an automatic 3D shape instantiation could be achieved by automatic multiple-class marker segmentation and hence automatic multiple-class marker center determination. Firstly, the markers were designed into five different shapes. Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy projections of customized markers into five classes and hence to determine the marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the network architecture, equally-weighted loss function for initial marker segmentation, and then equally-weighted focal loss function for improving the initial marker segmentation. This proposed network outperformed traditional Weighted U-Net on the class-imbalance segmentation in this paper with reducing one hyperparameter - the weight. An overall mean Intersection over Union (mIoU) of 0.6943 was achieved on 78 testing images, where 81.01 % markers were segmented with a center position error <; 1.6mm. Comparable accuracy of 3D shape instantiation was also achieved and stated. The data, trained models and TensorFlow codes are available on-line.

Increasing demands in endovascular intervention have motivated technical skill training and competency-based measures of performance. However, there are no well-established online metrics for technical skill assessment; few studies have explored operator behavioral patterns from catheter motion and operator hand motions. This paper proposes a platform for active online training and objective assessment of endovascular skills, through learning optimum catheter motions from multiple demonstrations. An ungrounded hand-held haptic device for providing intuitive haptic guidance to novice users based on this learnt information is also proposed. Statistical models are implemented to extract the underlying catheter motion patterns, and utilize them for performance evaluation and haptic guidance. The results show significant improvements in endovascular navigation for inexperienced operators. Finer catheter motions were achieved with the provided haptic guidance. The results suggest that the proposed platform can be integrated into current clinical training setups, and motivate the improvement of endovascular training platforms with better realism.

Despite the increased use of robotic catheter navigation systems, and the growing interest in surgical skill evaluation in the field of endovascular intervention, there is a lack of objective and quantitative metrics for performance evaluation. So far very little research has studied operator behavioral patterns using catheter kinematics, operator forces and motions, and catheter-tissue interactions. This paper proposes a framework for automated and objective assessment of performance by measuring catheter-tissue contact forces and operator motion patterns across different skill levels, and using language models to learn the underlying force and motion patterns that are characteristic of skill. Discrete HMMs are utilized to model operator behavior for varying skill levels performing different catheterization tasks, resulting in cross-validation classification accuracies of 94% (expert) and 98% (novice) using the force-based skill models, as well as 83% (expert) and 94% (novice) using the motion-based models. The results motivate the design of improved metrics for endovascular skill assessment with further applications towards performance evaluation of robot-assisted endovascular catheterization.