Enhanced segmentation and skeletonization for endovascular surgical planning

Cheng, Irene; Firouzmanesh, Amirhossein; Lelevé, Arnaud; Shen, Rui; Moreau, Richard; Brizzi, Vincenzo; Pham, Minh Tu; Redarce, Tanneguy; Lermusiaux, Patrick; Basu, Anup

doi:10.1117/12.911090

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…This trajectory is often generated according to the available preoperative geometry of the vessel, which is obtained through preoperative CT/MR scans Zhou et al (2016). Vascular centerlines are typically used as a reference trajectory, with researchers studying skeletonization of preoperative images for extracting blood vessel shapes and centerlines Cheng et al (2012); Li et al (2021).…”

Section: Dynamic Modeling and Formulation Of Multi-actuator Soft Cath...mentioning

confidence: 99%

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

et al. 2022

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Catheter-based endovascular interventional procedures have become increasingly popular in recent years as they are less invasive and patients spend less time in the hospital with less recovery time and less pain. These advantages have led to a significant growth in the number of procedures that are performed annually. However, it is still challenging to position a catheter in a target vessel branch within the highly complicated and delicate vascular structure. In fact, vessel tortuosity and angulation, which cause difficulties in catheterization and reaching the target site, have been reported as the main causes of failure in endovascular procedures. Maneuverability of a catheter for intravascular navigation is a key to reaching the target area; ability of a catheter to move within the target vessel during trajectory tracking thus affects to a great extent the length and success of the procedure. To address this issue, this paper models soft catheter robots with multiple actuators and provides a time-dependent model for characterizing the dynamics of multi-actuator soft catheter robots. Built on this model, an efficient and scalable optimization-based framework is developed for guiding the catheter to pass through arteries and reach the target where an aneurysm is located. The proposed framework models the deflection of the multi-actuator soft catheter robot and develops a control strategy for movement of catheter along a desired trajectory. This provides a simulation-based framework for selection of catheters prior to endovascular catheterization procedures, assuring that given a fixed design, the catheter is able to reach the target location. The results demonstrate the benefits that can be achieved by design and control of catheters with multiple number of actuators for navigation into small vessels.

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Section: Dynamic Modeling and Formulation Of Multi-actuator Soft Cath...mentioning

confidence: 99%

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

et al. 2022

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“…Commercial robotic system such as the Sensei X system (Hansen Medical, Mountain View, CA, USA) integrated 3D electroanatomic mapping (EAM) technology for improved navigation of the robotic catheter [10]. Other research has studied skeletonization techniques, extracted from CT angiography (CTA), for extracting blood vessel centerlines towards efficient path planning for endovascular surgical tools [11]. More recently, a cooperative robotic catheterization platform was developed for adapting learned trajectories to different vascular anatomies using shared-control navigation [12].…”

Section: Introductionmentioning

confidence: 99%

A learning based training and skill assessment platform with haptic guidance for endovascular catheterization

Chi

Rafii-Tari

Liu

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper improves a semi-automatic robotic catheterization platform based on previous works [9] by proposing a method to address subject variability. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among subjects. Statistical modeling is implemented to encode the catheter motions of both proximal and distal sites from demonstrations of one vascular model. Non-rigid registration is applied to find a warping function to map catheter tip trajectories onto other anatomically-similar but shape/scale/orientation different models. Such function can finally generate a robot trajectory to conduct a collaborative catheratization task. Experiments investigate the proposed method in different vascular phantoms. The success rate for semi-automatic cannulation is high, which suggest the method can be potentially applied to different endovascular tasks and vasculature. The proposed robotic approach also show significant improvements in the quality of catheterization over manual approach by achieving smoother and safer catheter paths and reducing contact forces. This work provides insights into catheter task planning and an improved design of handson, ergonomic catheter navigation robots.

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Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization

et al. 2018

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PurposeEndovascular intervention is limited by two-dimensional intraoperative imaging and prolonged procedure times in the presence of complex anatomies. Robotic catheter technology could offer benefits such as reduced radiation exposure to the clinician and improved intravascular navigation. Incorporating three-dimensional preoperative imaging into a semiautonomous robotic catheterization platform has the potential for safer and more precise navigation. This paper discusses a semiautonomous robotic catheter platform based on previous work (Rafii-Tari et al., in: MICCAI2013, pp 369–377. https://doi.org/10.1007/978-3-642-40763-5_46, 2013) by proposing a method to address anatomical variability among aortic arches. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among patient-specific anatomies.MethodsStatistical modeling is implemented to encode the catheter motions of both proximal and distal sites based on cannulation data obtained from a single phantom by an expert operator. Non-rigid registration is applied to obtain a warping function to map catheter tip trajectories into other anatomically similar but shape/scale/orientation different models. The remapped trajectories were used to generate robot trajectories to conduct a collaborative cannulation task under flow simulations. Cross-validations were performed to test the performance of the non-rigid registration. Success rates of the cannulation task executed by the robotic platform were measured. The quality of the catheterization was also assessed using performance metrics for manual and robotic approaches. Furthermore, the contact forces between the instruments and the phantoms were measured and compared for both approaches.ResultsThe success rate for semiautomatic cannulation is 98.1% under dry simulation and 94.4% under continuous flow simulation. The proposed robotic approach achieved smoother catheter paths than manual approach. The mean contact forces have been reduced by 33.3% with the robotic approach, and 70.6% less STDEV forces were observed with the robot.ConclusionsThis work provides insights into catheter task planning and an improved design of hands-on ergonomic catheter navigation robots.

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Enhanced segmentation and skeletonization for endovascular surgical planning

Cited by 6 publications

References 20 publications

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

A learning based training and skill assessment platform with haptic guidance for endovascular catheterization

Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization

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