Planning High-Quality Motions for Concentric Tube Robots in Point Clouds via Parallel Sampling and Optimization

Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs

PURPOSE

We propose the utilization of patient-specific concentric-tube robots (CTRs) whose designs are optimized to enhance their volumetric reachability of the renal stone, thus reducing the morbidities associated with percutaneous nephrolithotomy procedures. By employing a nested optimization-driven scheme, this work aims to determine a single surgical tract through which the patient-tailored CTR is deployed. We carry out a sensitivity analysis on the combined percutaneous access and optimized CTR design with respect to breathing-induced excursion of the kidneys based on preoperative images. Further, an investigation is also performed of the appropriateness and effectiveness of the percutaneous access provided by the proposed algorithm compared to that of an expert urologist.

METHODS

The method is based on an ellipsoidal approximation to the renal calculi and a grid search over candidate skin areas and available renal calyces using an anatomically constrained kinematic mapping of the CTR. Percutaneous access is selected for collision-free CTR deployment to the centroid of the stones with minimal positional error at the renal calyx. Further optimization of the CTR design results in a robot tailored to the therapeutic anatomical features of each clinical case. The study examined 14 sets of clinical data of PCNL patients, analyzing stone reachability using preoperative images and breathing-induced motions of the kidney. An experienced urologist qualitatively assessed the adequacy of percutaneous access generated by the algorithm.

RESULTS

An assessment conducted by an expert urologist found that the percutaneous accesses produced by the proposed approach were found to be comparable to those chosen by the expert surgeon in most clinical cases. The simulated results demonstrated a mean volume coverage of 81.6 % ± 19.6 % for static anatomy and 63.7 % ± 27.7 % and 69.0 % ± 25.4 % when considering a 1 cm excursion of the kidney in the craniocaudal directions due to respiration or tool-tissue interaction.

CONCLUSION

The optimization-driven scheme for determining a single tract surgical plan, coupled with the use of a patient-specific CTR, shows promising results for improving percutaneous access in PCNL procedures. This approach clearly shows the potential for enhancing the quality and suitability of percutaneous accesses, addressing the challenges posed by staghorn and non-staghorn stones during PCNL procedures. Further research involving clinical validation is necessary to confirm these findings and explore the potential clinical benefits of the approach.

Path planning for endovascular catheterization under curvature constraints via two-phase searching approach

Purpose

Planning a safe path for flexible catheters is one of the major challenges of endovascular catheterization. State-of-the-art methods rarely consider the catheter curvature constraint and reduced computational time of path planning which guarantees the possibility to re-plan the path during the actual operation.

Methods

In this manuscript, we propose a fast two-phase path planning approach under the robot curvature constraint. Firstly, the vascular structure is extracted and represented by vascular centerlines and corresponding vascular radii. Then, the path is searched along the vascular centerline using breadth first search (BFS) strategy and locally optimized via the genetic algorithm (GA) to satisfy the robot curvature constraint. This approach (BFS-GA) is able to respect the robot curvature constraint while keeping it close to the centerlines as much as possible. We can also reduce the optimization search space and perform parallel optimization to shorten the computational time.

Results

We demonstrate the method’s high efficiency in two-dimensional and three-dimensional space scenarios. The results showed the planner’s ability to satisfy the robot curvature constraint while keeping low computational time cost compared with sampling-based methods. Path replanning in femoral arteries can reach an updating frequency at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.4\pm 2.3$$\end{document}6.4±2.3Hz.

Conclusion

The presented work is suited for surgical procedures demanding satisfying curvature constraints while optimizing specified criteria. It is also applicable for curvature constrained robots in narrow passages.

Learning the Complete Shape of Concentric Tube Robots

Concentric tube robots, composed of nested pre-curved tubes, have the potential to perform minimally invasive surgery at difficult-to-reach sites in the human body. In order to plan motions that safely perform surgeries in constrained spaces that require avoiding sensitive structures, the ability to accurately estimate the entire shape of the robot is needed. Many state-of-the-art physics-based shape models are unable to account for complex physical phenomena and subsequently are less accurate than is required for safe surgery. In this work, we present a learned model that can estimate the entire shape of a concentric tube robot. The learned model is based on a deep neural network that is trained using a mixture of simulated and physical data. We evaluate multiple network architectures and demonstrate the model's ability to compute the full shape of a concentric tube robot with high accuracy.