Endovascular catheterization is an intervention which offers a low risk alternative to open surgery in many patients.Today's interventions rely heavily on fluoroscopic imaging to guide interventionalists. Fluoroscopy only produces 2D visualization of the catheter and also exposes both the patient and interventionalists to harmful radiation. Different approaches have been proposed to overcome the limitations of fluoroscopy. Fiber Bragg Grating (FBG)-based shape sensing is becoming popular to reconstruct the catheter shape. Multi-core fibers with parallel optical cores are interesting as they allow 3D shape reconstruction with a single fiber. A common issue with FBG-based shape sensing is its sensitivity to variations in twist. Even small amounts of twist can significantly impact the overall shape reconstruction accuracy. This work proposes a novel approach which combines electromagnetic tracking (EMT), FBG-based shape sensing, and sparse fluoroscopic images. The method provides realtime 3D visualization of the catheter without the need of continuous fluoroscopy. A unique feature of the proposed method is the selective use of imaging for dynamic twist-compensation of the FBG sensor. The proposed sensor-fusion method improved 3D reconstruction accuracy. Real-world in-vitro experiments promising results. For a catheter with an embedded fiber length of 170 mm, the proposed approach the 3D shape with a median root-mean-square (rms) error of 0.39 mm and an interquartile range of 0.10 mm in the 2D experiment in which the catheter was bent in a plane. A median rms error of 0.54 mm and an interquartile range of 0.07 mm were achieved in the 3D experiments.

<p>In this paper, we propose a novel method to improve the shape sensing accuracy of FBG for catheter by fusing FBG-based sensed shape with sparse fluoroscopic images. The main advantage of the new proposed method compared to other methods are the limited number in fluoroscopic image used during procedure while it still maintains high precision real-time 3D visualization of the catheter. To demonstrate the performance of the proposed method 2D and 3D dynamic experiments were carried out and they shows promising results. For a catheter with an embedded fiber length of 170 mm, the proposed approach can reconstruct the 3D shape with a median root mean square error of 0.54 mm were seen in the 3D experiments compared to the traditional approach of using FBG alone of 0.86 mm.</p>

In cardiovascular interventions, when steering catheters and especially robotic catheters, great care should be paid to prevent applying too large forces on the vessel walls as this could dislodge calcifications, induce scars or even cause perforation. To address this challenge, this paper presents a novel compliant motion control algorithm that relies solely on position sensing of the catheter tip and knowledge of the catheter's behavior. The proposed algorithm features a data-driven tip position controller. The controller is trained based on a so-called control Long Short-Term Memory Network (control-LSTM). Trajectory following experiments are conducted to validate the quality of the proposed control-LSTM. Results demonstrated superior positioning capability with sub-degree precision of the new approach in the presence of severe rate-dependent hysteresis. Experiments both in a simplified setup as well as in an aortic phantom further show that the proposed approach allows reducing the interaction forces with the environment by around 70%. This work shows how deep learning can be exploited advantageously to avoid tedious modeling that would be needed to precisely steer continuum robots in constrained environments such as the patient's vasculature.

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