Design and performance evaluation of a master controller for endovascular catheterization

Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions

Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.

A marker-based contactless catheter-sensing method to detect surgeons' operations for catheterization training systems

It is challenging to position a catheter or a guidewire within a patient's complicated and delicate vascular structure due to the lack of intuitive visual feedback by only manipulating the proximal part of the surgical instruments. Training is therefore critical before an actual surgery because any mistake due to the surgeon's inexperience can be fatal for the patient. The catheter manipulation skills of experienced surgeons can be useful as input for training novice surgeons. However, few research groups focused on designs with consideration of the contactless catheter motion measurement, which allows obtaining expert surgeons' catheter manipulation trajectories whilst still allowing them to employ an actual catheter and apply conventional pull, push and twist of the catheter as used in bedside intravascular interventional surgeries. In this paper, a novel contactless catheter-sensing method is proposed to measure the catheter motions by detecting and tracking a passive marker with four feature-point groups. The passive marker is designed to allow simultaneously sensing the translational and rotational motions of the input catheter. Finally, the effectiveness of the proposed contactless catheter-sensing method is validated by conducting a series of comparison experiments. The accuracy and error analysis are quantified based on the absolute error, relative error, mean absolute error, and the success rate of the detection.