Atrial fibrillation is the most prevalent form of persistent cardiac arrhythmia, causing uncoordinated heart beating, due to faulty electrical conductivity of the myocardium [1]. Cardiac ablation is a minimally-invasive procedure aiming at ablating the internal atrial walls to restore healthy electrical conduction. A transseptal sheath is positioned across from the right to left atrium, through a small puncture in the septum, reached from the femoral vein through the inferior vena cava (see Fig. 1). The ablating catheter is inserted through the sheath and used to ablate around the pulmonary veins. Robotic ablation procedures have the potential of cre- ating more uniform lesions, reducing operator fatigue and extending catheter reach. Magnetic actuation has attracted particular attention [2], since it can stabilize contact between the catheter tip and the wall of the beating heart - via magnetic attraction. However, it often requires large-scale actuation units, which are hard to integrate with the clinical workflow. This work introduces a novel magnetic navigation sys- tem for cardiac ablation (see Fig. 1), formed from two key elements: a magnetic ablation catheter [3] consisting of a chain of spherical permanent magnets (ball chain); and an actuation system comprised of two permanent magnets undergoing pure rotation. These two elements enable design of actuation units of reduced size: the large magnetic content of the ball chain allows use of smaller actuation magnets and pure rotation of the actuation magnets permits the use of a compact cart- based system. In our experiments, with a scaled version of the proposed platform (see Fig. 2), we demonstrate that pure rotation of the actuating magnet is sufficient for navigating around the pulmonary veins. Catheter control using a joystick is compared with manual operation of a standard ablation catheter. During learning, a novice can perform navigation tasks 1.6 times faster than with a manual catheter. Once proficient, an operator can navigate hard- to-reach areas 21% faster using the robot.
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Pittiglio et al. (2024) studied this question.
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