Magnetic soft microfiberbots for robotic embolization.

Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately occluding the target lesion to reduce the blood supply, embolization has been widely used clinically to treat cerebral aneurysms and brain tumors. Conventional embolization is usually performed by threading a catheter through blood vessels to the target lesion, which is often limited by the poor steerability of the catheter in complex neurovascular networks, especially in submillimeter regions. Here, we propose magnetic soft microfiberbots with high steerability, reliable maneuverability, and multimodal shape reconfigurability to perform robotic embolization in submillimeter regions via a remote, untethered, and magnetically controllable manner. Magnetic soft microfiberbots were fabricated by thermal drawing magnetic soft composite into microfibers, followed by magnetizing and molding procedures to endow a helical magnetic polarity. By controlling magnetic fields, magnetic soft microfiberbots exhibit reversible elongated/aggregated shape morphing and helical propulsion in flow conditions, allowing for controllable navigation through complex vasculature and robotic embolization in submillimeter regions. We performed in vitro embolization of aneurysm and tumor in neurovascular phantoms and in vivo embolization of a rabbit femoral artery model under real-time fluoroscopy. These studies demonstrate the potential clinical value of our work, paving the way for a robotic embolization scheme in robotic settings.

Learning-based intelligent trajectory planning for auto navigation of magnetic robots.

Introduction: Electromagnetically controlled small-scale robots show great potential in precise diagnosis, targeted delivery, and minimally invasive surgery. The automatic navigation of such robots could reduce human intervention, as well as the risk and difficulty of surgery. However, it is challenging to build a precise kinematics model for automatic robotic control because the controlling process is affected by various delays and complex environments. Method: Here, we propose a learning-based intelligent trajectory planning strategy for automatic navigation of magnetic robots without kinematics modeling. The Long Short-Term Memory (LSTM) neural network is employed to establish a global mapping relationship between the current sequence in the electromagnetic actuation system and the trajectory coordinates. Result: We manually control the robot to move on a curved path 50 times to form the training database to train the LSTM network. The trained LSTM network is validated to output the current sequence for automatically controlling the magnetic robot to move on the same curved path and the tortuous and branched new paths in simulated vascular tracks. Discussion: The proposed trajectory planning strategy is expected to impact the clinical applications of robots.