A semi-autonomous robot control based on bone layer transition detection for a safe pedicle tapping

PURPOSE

Automatic robotic platforms for robot-aided spinal surgery are mostly employed for drilling the pedicle screw path and do not adapt the tool rotational speed depending on the variation of the bone density. This feature is highly desirable in control strategies for robot-aided pedicle tapping, which may result in a poor quality thread if the surgical tool speed is not adequately tuned depending on the bone density to be threaded. Therefore, the objective of this paper is to propose a novel semi-autonomous control for robot-aided pedicle tapping that is able to (i) identify the bone layer transition, (ii) adapt the tool velocity depending on the detected bone layer density and (iii) stop the tool tip before propulsion of the bone boundaries.

METHODS

The proposed semi-autonomous control for pedicle tapping consists of: (i) a hybrid position/force control loop that allows the surgeon to move the surgical tool along a pre-planned axis and (ii) a velocity control loop that allows him/her to finely tune the tool rotational speed by modulating the tool-bone interaction force along the same axis. The velocity control loop integrates also a bone layer transition detection algorithm that dynamically limits the tool velocity depending on the bone layer density. The approach was tested on the Kuka LWR4+ provided with an actuated surgical tapper which was used to tap a wood specimen simulating the bone layer density characteristics and bovine bones.

RESULTS

A normalized maximum time delay in the bone layer transition detection of 0.25 was achieved by the experiments. A success rate of [Formula: see text] was achieved for all the tested tool velocities. The proposed control achieved a maximum steady-state error of 0.4 rpm.

CONCLUSION

The study demonstrated high capability of the proposed approach to i) promptly detect transition among the specimen layers and ii) adapt the tool velocities depending on the detected layers.

Magnetically steerable catheters: State of the art review

Magnetically steerable catheters (MSCs) have caught the interest of researchers due to their various potential uses in clinical applications, for example, minimally invasive surgery. Many significant advances in the design, implementation and analysis of MSCs have been accomplished in the last decade. This review concentrates on the configurations of current MSCs with an in depth look at control of the device and the specific workspace. This review also evaluates MSCs and references possible future system designs and difficulties. The concept of magnetic manipulation is briefly presented. Then, by category, the MSC is introduced. Following that, a discussion of future works and challenges of the review systems is provided. The conclusions are finally addressed.

A newly developed image fusion algorithm between CECT image and CT image: A feasibility study

Cancer cases have been on the rise over the world. Cancer treatment can benefit from an early accurate diagnosis. Percutaneous needle biopsy under the guidance of CT images is the most common method to obtain tumor samples for accurate diagnosis. However, due to the lack of vascular information in the CT images, the biopsy procedure is at great risk, especially for the tumor surrounded by vessels. In this study, a biomechanical model and surface elastic registration-based fusion algorithm were developed to map the vessels from contrast-enhanced CT images of the liver and lung to the corresponded CT image. Radiologists could observe vessels in the CT images during the biopsy procedure so that the risk can be decreased. The developed algorithm was tested through 20 groups of lung data and 16 groups of liver data. The results show that the fusion errors (mean ± standard deviation) were 2.35 ± 0.85, 2.08 ± 0.41, 2.31 ± 0.49, and 2.37 ± 0.62 mm for portal vein, hepatic vein, pulmonary artery, and pulmonary vein, respectively. The accuracy of this method was satisfied in clinical application

Computer-assisted image-based risk analysis and planning in lung surgery - a review

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model

PURPOSE

Due to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work.

METHODS

A patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under different clinical parametric combinations.

RESULTS

Real 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66 mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2 mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention.

CONCLUSIONS

The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.

A Soft Robot for Peripheral Lung Cancer Diagnosis and Therapy

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagnostic procedure is limited in ability to explore deep into the periphery of the lung where cancer can remain undetected. In this study, we present design, modeling, fabrication, and testing of a one degree of freedom soft robot with integrated diagnostic and interventional capabilities as well as vision sensing. The robot can be deployed through the working channel of commercial bronchoscopes or used as a stand-alone system as it integrates a micro camera to provide vision sensing and controls to the periphery of the lung. The small diameter (2.4 mm) of the device allows navigation in branches deeper in the lung, where current devices have limited reachability. We have performed mechanical characterizations of the robotic platform, including blocked force, maximum bending angle, maximum angular velocity, and workspace, and assessed its performance in in vitro and ex vivo experiments. We have developed a computer vision algorithm, and validated it in in vitro conditions, to autonomously align the robot to a selected branch of the lung and aid the clinician (by means of a graphical user interface) during navigation tasks and to perform robot-assisted stabilization in front of a lesion, with automated tracking and alignment.