Endovascular intervention is a minimally invasive method for treating cardiovascular diseases. Although fluoroscopy, known for real-time catheter visualization, is commonly used, it exposes patients and physicians to ionizing radiation and lacks depth perception due to its 2D nature. To address these limitations, a study was conducted using teleoperation and 3D visualization techniques. This <italic>in-vitro</italic> study involved the use of a robotic catheter system and aimed to evaluate user performance through both subjective and objective measures. The focus was on determining the most effective modes of interaction. Three interactive modes for guiding robotic catheters were compared in the study: 1) Mode GM, using a gamepad for control and a standard 2D monitor for visual feedback; 2) Mode GH, with a gamepad for control and HoloLens providing 3D visualization; and 3) Mode HH, where HoloLens serves as both control input and visualization device. Mode GH outperformed other modalities in subjective metrics, except for mental demand. It exhibited a median tracking error of 4.72 mm, a median targeting error of 1.01 mm, a median duration of 82.34 s, and a median natural logarithm of dimensionless squared jerk of 40.38 in the <italic>in-vitro</italic> study. Mode GH showed 8.5&#x0025;, 4.7&#x0025;, 6.5&#x0025;, and 3.9&#x0025; improvements over Mode GM and 1.5&#x0025;, 33.6&#x0025;, 34.9&#x0025;, and 8.1&#x0025; over Mode HH for tracking error, targeting error, duration, and dimensionless squared jerk, respectively. To sum up, the user study emphasizes the potential benefits of employing HoloLens for enhanced 3D visualization in catheterization. The user study also illustrates the advantages of using a gamepad for catheter teleoperation, including user-friendliness and passive haptic feedback, compared to HoloLens. To further gauge the potential of using a more traditional joystick as a control input device, an additional study utilizing the Haption Virtuose^TM robot was conducted. It reveals the potential for achieving smoother trajectories, with a 38.9&#x0025; reduction in total path length compared to a gamepad, potentially due to its larger range of motion and single-handed control.

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI for reaching the target area. Current research efforts are directed toward increasing the Level of Autonomy (LoA) during the navigation phase. One key ingredient for autonomous navigation is Motion Planning (MP) techniques. This paper provides an overview of MP techniques categorizing them based on LoA. Our analysis investigates advances for the different clinical scenarios. Through a systematic literature analysis using the PRISMA method, the study summarizes relevant works and investigates the clinical aim, LoA, adopted MP techniques, and validation types. We identify the limitations of the corresponding MP methods and provide directions to improve the robustness of the algorithms in dynamic intraluminal environments. MP for IP and EI can be classified into four subgroups: node, sampling, optimization, and learning-based techniques, with a notable rise in learning-based approaches in recent years. One of the review's contributions is the identification of the limiting factors in IP and EI robotic systems hindering higher levels of autonomous navigation. In the future, navigation is bound to become more autonomous, placing the clinician in a supervisory position to improve control precision and reduce workload.

Optical fiber-based shape sensing is gaining popularity in cardiac catheterization lately. Typically, these procedures are taking place under the guidance of fluoroscopy. However, fluoroscopy has several disadvantages. Thanks to fiber optic shape sensing and Electromagnetic Tracking (EMT), the 3D catheter shape can now be tracked in real-time without the need for fluoroscopy. Traditional optical fiber and EMT-based shape tracking methods have the drawback of the highest shape sensing error at the tip. The information offered by the EMT sensors is used mainly to localize the estimated shape in a fixed coordinate frame. In this letter, a novel approach for tracking the catheter is introduced to address the aforementioned problem. The catheter shape is directly reconstructed in the EMT coordinate frame by approximating the catheter shape by a number of Bézier curves while taking into account the curvatures measured by the optical fiber. Both 2D and 3D shape sensing experiments are conducted. The results of the 3D experiment show that the proposed method reduces the mean shape tracking error by approximately 38% (from 12.1 mm to 5.4 mm for a sensed length of 540 mm long) compared to the traditional method where the same number of sensors are used.

In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many characteristic parameters. A further limitation of traditional approaches is that they require either multiple fibers, whose location relative to the centerline must be precisely known (calibrated), or a single multicore fiber whose position typically coincides with the neutral line. The proposed method addresses this limitation and, thus, allows shape sensing based on a single multicore fiber placed off-center. This helps in miniaturizing and leaves the central channel available for other purposes. The proposed approach was compared to a recent state-of-the-art model-based shape sensing approach. A two-degree-of-freedom benchtop fluidics-driven catheter system was built to validate the proposed ANN. The proposed ANN-based shape sensing approach was evaluated on a 40-mm-long steerable continuum robot in both 3-D free-space and 2-D constrained environments, yielding an average shape sensing error of 0.24 and 0.49 mm, respectively. With these results, the superiority of the proposed approach compared to the recent model-based shape sensing method was demonstrated.

Minimally invasive catheter-based interventions normally take place under the guidance of fluoroscopy. However, fluoroscopy is harmful to both patients and clinicians. Moreover, it only offers 2-D shape visualization of flexible devices. To solve the problem of harmful radiation and offer 3-D pose and shape information, recent studies propose a combination of electromagnetic tracking (EMT) sensors and multicore fiber Bragg grating (FBG) fiber sensing. However, for robust localization, at least two EMT sensors are required to be attached to each multicore fiber. This may make the catheter overly complex and fragile. Furthermore, the inability of multicore FBG fibers to distinguish between twist-induced strain and bend-induced strain impacts shape sensing accuracy. This article proposes a new approach offering a precise shape sensing method that is robust against torsional twists and exploits symmetry and geometry to compensate for limited sensing information. The proposed approach originates from the observation that many interventional procedures employ a plurality of concentric instruments. By distributing sensors over these instruments, the complexity per instrument can be kept acceptable. The proposed sensor fusion approach ensures robust and superior shape reconstruction. Experiments in 3-D with ground truth generated by a stereo vision system have been done and yielded promising results. Compared to the state-of-the-art methods, the presented framework uses only half of the required EMT sensors per instrument resulting in significant spatial conservation while improving the catheter shape tracking accuracy by 57%.

Continuum robots such as robotic catheters are increasingly being used in minimally invasive surgery. Compliance contributes to enhanced safety during e.g. catheter insertion, however, estimation of contact force and location may help clinicians avoiding exerting excessive force. Ultimately this could lead to faster and safer interventions. Researchers proposed force sensors integrated in the catheter tip in the past. However, such sensors add extra complexity to the catheter design. Also, tip force sensors do not provide insights on forces that act along the catheter length. This paper proposes a data-driven approach for localizing contact forces that appear over the length of the catheter. The proposed approach consists of a collision detection method and a contact localization method. The framework only requires the measurement of the catheter's shape which can be done by an embedded multi-core Fiber Bragg Grating fiber. The method was validated experimentally with a 3D-printed continuum robot with an integrated multi-core fiber. A second contact localization method which is based on identifying the discontinuity in the measured curvature, is also implemented and compared with the proposed method. The static and dynamic experiments show a mean average localization error of 2.3 mm and 4.3 mm which correspond to respectively 3.3% and 6.1% of a 70 mm long flexible robot. These findings demonstrate that the proposed framework outperforms the previous methods and yield promising results. The contact state estimation algorithm can detect collisions in at most approximately 1.08s.

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.

This work focuses on the design of a new device (called ChoRe) to place artificial chords in the mitral valve structure during a trans-catheter procedure. The aim of the device is to restore the correct functionality of the valve and solve mitral valve regurgitation, that is, a common consequence of chordae tendineae rupture. An analysis of the requirements was carried out and used to design and develop a first functional prototype. The resulting device was able to connect artificial chords at the posterior leaflet of the mitral valve and at the apex of the left ventricle, also allowing the control of the artificial chord length. The ChoRe was tested ex-vivo in bovine hearts. The qualitative assessment of the ChoRe focused on the performance of the device and preliminary evaluation of the procedure time. Results demonstrated that the device is able to create a top and bottom fixation in an average time of 3.45 ± 1.44 min. Future improvements will focus on enhancing the connection at the leaflet, as well as the overall functionality, in order to guarantee better control of the artificial chord length. This work shows future potentials for more patient-specific treatments in trans-catheter scenarios for mitral valve repair.

Needles are advanced tools commonly used in minimally invasive medical procedures. The accurate manoeuvrability of flexible needles through soft tissues is strongly determined by variations in tissue stiffness, which affects the needle-tissue interaction and thus causes needle deflection. This work presents a variable stiffness mechanism for percutaneous needles capable of compensating for variations in tissue stiffness and undesirable trajectory changes. It is composed of compliant segments and rigid plates alternately connected in series and longitudinally crossed by four cables. The tensioning of the cables allows the omnidirectional steering of the tip and the stiffness tuning of the needle. The mechanism was tested separately under different working conditions, demonstrating a capability to exert up to 3.6 N. Afterwards, the mechanism was integrated into a needle, and the overall device was tested in gelatine phantoms simulating the stiffness of biological tissues. The needle demonstrated the capability to vary deflection (from 11.6 to 4.4 mm) and adapt to the inhomogeneity of the phantoms (from 21 to 80 kPa) depending on the activation of the variable stiffness mechanism. [Figure not available: see fulltext.]