Toward Autonomous Pulmonary Artery Catheterization: A Learning-based Robotic Navigation System.

Providing imaging during interventional treatments of cardiovascular diseases is challenging. Magnetic Resonance Imaging (MRI) has gained popularity as it is radiation-free and returns high resolution of soft tissue. However, the clinician has limited access to the patient, e.g., to their femoral artery, within the MRI scanner to accurately guide and manipulate an MR-compatible catheter. At the same time, communication will need to be maintained with a clinician, located in a separate control room, to provide the most appropriate image to the screen inside the MRI room. Hence, there is scope to explore the feasibility of how autonomous catheterization robots could support the steering of catheters along trajectories inside complex vessel anatomies.In this paper, we present a Learning from Demonstration based Gaussian Mixture Model for a robot trajectory optimisation during pulmonary artery catheterization. The optimisation algorithm is integrated into a 2 Degree-of-Freedom MR-compatible interventional robot allowing for continuous and simultaneous translation and rotation. Our methodology achieves autonomous navigation of the catheter tip from the inferior vena cava, through the right atrium and the right ventricle into the pulmonary artery where an interventions is performed. Our results show that our MR-compatible robot can follow an advancement trajectory generated by our Learning from Demonstration algorithm. Looking at the overall duration of the intervention, it can be concluded that procedures performed by the robot (teleoperated or autonomously) required significantly less time compared to manual hand-held procedures.

Meshless Simulation of Multi-site Radio Frequency Catheter Ablation through the Fragile Points Method.

Computational models for radio frequency catheter ablation (RFCA) of cardiac arrhythmia have been developed and tested in conditions where a single ablation site is considered. However, in reality arrhythmic events are generated at multiple sites which are ablated during treatment. Under such conditions, heat accumulation from several ablations is expected and models should take this effect into account. Moreover, such models are solved using the Finite Element Method which requires a good quality mesh to ensure numerical accuracy. Therefore, clinical application is limited since heat accumulation effects are neglected and numerical accuracy depends on mesh quality. In this work, we propose a novel meshless computational model where tissue heat accumulation from previously ablated sites is taken into account. In this way, we aim to overcome the mesh quality restriction of the Finite Element Method and enable realistic multi-site ablation simulation. We consider a two ablation sites protocol where tissue temperature at the end of the first ablation is used as initial condition for the second ablation. The effect of the time interval between the ablation of the two sites is evaluated. The proposed method demonstrates that previous models that do not account for heat accumulation between ablations may underestimate the tissue heat distribution.Clinical relevance- The proposed computational model may be used to build and update a heat map for ablation guidance taking into account the contribution from previously ablated sites. Being a meshless model, it does not require significant input from the user during preprocessing. Therefore, it is suitable for application in a clinical setting.

Using visual cues to enhance haptic feedback for palpation on virtual model of soft tissue.

This paper explores methods that make use of visual cues aimed at generating actual haptic sensation to the user, namely pseudo-haptics. We propose a new pseudo-haptic feedback-based method capable of conveying 3D haptic information and combining visual haptics with force feedback to enhance the user's haptic experience. We focused on an application related to tumor identification during palpation and evaluated the proposed method in an experimental study where users interacted with a haptic device and graphical interface while exploring a virtual model of soft tissue, which represented stiffness distribution of a silicone phantom tissue with embedded hard inclusions. The performance of hard inclusion detection using force feedback only, pseudo-haptic feedback only, and the combination of the two feedbacks was compared with the direct hand touch. The combination method and direct hand touch had no significant difference in the detection results. Compared with the force feedback alone, our method increased the sensitivity by 5%, the positive predictive value by 4%, and decreased detection time by 48.7%. The proposed methodology has great potential for robot-assisted minimally invasive surgery and in all applications where remote haptic feedback is needed.

Embedded electro-conductive yarn for shape sensing of soft robotic manipulators.

Flexible soft and stiffness-controllable surgical manipulators enhance the manoeuvrability of surgical tools during Minimally Invasive Surgery (MIS), as opposed to conventional rigid laparoscopic instruments. These flexible and soft robotic systems allow bending around organs, navigating through complex anatomical pathways inside the human body and interacting inherently safe with its soft environment. Shape sensing in such systems is a challenge and one essential requirement for precise position feedback control of soft robots. This paper builds on our previous work integrating multiple optical fibres into a soft manipulator to estimate the robot's pose using light intensity modulation. Here, we present an enhanced version of our embedded bending/shape sensor based on electro-conductive yarn. The new system is miniaturised and able to measure bending behaviour as well as elongation. The integrated yarn material is helically wrapped around an elastic strap and protected inside a 1.5mm outer-diameter stretchable pipe. Three of these resulting stretch sensors are integrated in the periphery of a pneumatically actuated soft manipulator for direct measurement of the actuation chamber lengths. The capability of the sensing system in measuring the bending curvature and elongation of the arm is evaluated.