Gui-Bin Bian

Design and evaluation of a bio-inspired robotic hand for percutaneous coronary intervention

The percutaneous coronary interventions (PCI) require complex operating skills of the interventional devices and make the surgeons being exposed to heavy X-ray radiation. Accurate delivery of the interventional devices and avoiding the radiation are especially important for the surgeons. This paper presents a novel dedicated dual-finger robotic hand (DRH) and a console to assist the surgeons to deliver the interventional devices in PCIs. The system is designed in the master-slave way which helps the surgeons to reduce the exposure to radiation. The mechanism of the DRH is bio-inspired and motions are decoupled in kinematics. In PCI procedures, the accuracy of the guidewire delivery and the catheter tip placement have significant effects on the surgical results. The performances of the DRH in delivering the guidewire and the balloon/stent catheter were evaluated by three surgical manipulations. The results show that the DRH has the ability to deliver the guidewire and the balloon/stent catheter precisely.

3D modeling of coronary arteries based on tubular-enhanced CURVES segmented regions for robotic surgical simulation

The visualization of the coronary vasculature is of utmost importance in interventional cardiology. Intravascular surgical robots assist the practitioners to perform the complex procedure while protecting them from the tremendous occupational hazards. Robotic surgical simulation aims to provide support for the learners in both efficiency and convenience. The blood vessels especially the coronary arteries with rich details are the key part of the anatomic scenario of the virtual training system. The variations in diameters and directions make the segmentation of the coronary arteries a difficult work. In this paper, a robust and semi-automatic approach for the segmentation of the coronary arteries is developed. The approach is based on the multi-scale tubular enhancement and an improved geodesic active contours model. The demonstrated approach firstly enhances the tubular objects by computing their “vesselness”. Next the edge potential maps are calculated based on the enhanced information. Meanwhile, the initial contours are generated by a modified fast marching method. Then the actual wave fronts evolution extracts the details of the coronary arteries. Finally the visualization model is organized based on the segmentation results by the marching cubes method. This approach has been proved successful for the visualization of the coronary arteries based on the CTA information.

A multi-body mass-spring model for virtual reality training simulators based on a robotic guide wire operating system

Generally, surgeons of minimally invasive surgery should possess good ability to coordinate their both hands. The manipulation of guide wire is considered a core skill. Obtaining that core skill to perform minimally invasive surgery requires training. In minimally invasive surgery, a surgical robot can assist doctors to position precisely and provide stable operation platform. Therefore, to develop a virtual reality simulators for training purpose based on a robotic guide wire operating system is an important and challenging subject. In this paper, a multi-body mass-spring model for simulating guide wire is presented and evaluated. In order to overcome the disadvantage of using mass-spring approach to model the guide wire, we propose a new collision detection algorithm and a new collision response algorithm. Finally, we test our guide wire with a complex and realistic 3D vascular model, which is selected from computer tomography database of real patients. The result shows that the virtual reality training simulators is effective and promising.

A collision response algorithm for 3D virtual reality minimally invasive surgery simulator

In recent years, rapid development of minimally invasive surgery has taken place. Virtual reality simulator enables the trainees to obtain the core catheter/guide wire handling skills and to decrease the error rate of operation prior to performing them on a real patient. In this paper, we present and evaluate a collision response algorithm and a force feedback computing method for simulating a catheter/guide wire in the interactive 3D virtual realty simulator based on a robotic catheter/guide wire operating system. In order to provide a real-time virtual environment, a multi-threading technology is used to accelerate the medical simulation procedure. Finally, we test the virtual catheter/guide wire with a complex and realistic 3D vascular model, which is generated from computed tomography angiography (CTA) series in DICOM datasets captured in a actual patient. The results show that the collision response algorithm in the system is effective and promising.

Fast and stable guidewire simulator for minimally invasive vascular surgery.

In recent years, minimally invasive vascular surgery is widely applied in treatment of cardiovascular diseases, and the manipulation of the guidewire is the essential skill for this surgery. Lots of time and money have to be taken to achieve the skill. In this paper, we present a multithreading guidewire simulator which can help the apprentice to gain the skill and modeling the guidewire is the core technique of the simulator. The guidewire is modeled by a fast and stable method based on the Cosserat theory of elastic rods. The method describes the behavior of the guidewire with the Lagrange equations of motion and it uses the penalty method to maintain constraints. We further propose a simplified solving procedure for the guidewire model. Finally, some experiments are conducted to evaluate the effectiveness of this model.In recent years, minimally invasive vascular surgery is widely applied in treatment of cardiovascular diseases, and the manipulation of the guidewire is the essential skill for this surgery. Lots of time and money have to be taken to achieve the skill. In this paper, we present a multithreading guidewire simulator which can help the apprentice to gain the skill and modeling the guidewire is the core technique of the simulator. The guidewire is modeled by a fast and stable method based on the Cosserat theory of elastic rods. The method describes the behavior of the guidewire with the Lagrange equations of motion and it uses the penalty method to maintain constraints. We further propose a simplified solving procedure for the guidewire model. Finally, some experiments are conducted to evaluate the effectiveness of this model.

A 3D virtual reality simulator for training of minimally invasive surgery

For the last decade, remarkable progress has been made in the field of cardiovascular disease treatment. However, these complex medical procedures require a combination of rich experience and technical skills. In this paper, a 3D virtual reality simulator for core skills training in minimally invasive surgery is presented. The system can generate realistic 3D vascular models segmented from patient datasets, including a beating heart, and provide a real-time computation of force and force feedback module for surgical simulation. Instruments, such as a catheter or guide wire, are represented by a multi-body mass-spring model. In addition, a realistic user interface with multiple windows and real-time 3D views are developed. Moreover, the simulator is also provided with a human-machine interaction module that gives doctors the sense of touch during the surgery training, enables them to control the motion of a virtual catheter/guide wire inside a complex vascular model. Experimental results show that the simulator is suitable for minimally invasive surgery training.

A fast and stable guidewire model for minimally invasive vascular surgery based on Lagrange multipliers

The simulator based on interactive virtual reality can solve many drawbacks in traditional surgery training, and it is widely used to train apprentices. A real-time and realistic guidewire model is a challenging task for the simulator, which is used to simulate minimally invasive vascular surgery. In this paper, we propose a fast and stable physical model to simulate the behavior of the guidewire. And Cosserat theory of elastic rods is used to simulate the bending and twisting of the guidewire, the inextensibility of which is maintained with Lagrange multipliers. Then an operation method is proposed to control the virtual guidewire effectively. Finally, some experiments are conducted to demonstrate the effectiveness of our guidewire model and the operation method.

A 3-DOF compact haptic interface for endoscopic endonasal approach surgery simulation

Endoscopic endonasal approach surgery is now the preferred treatment for most pituitary and related skull base tumors. However, this procedure requires a high level of hands-on skills and rich clinical experience. During the operation, haptic feedback, as the only one sense of bidirectional information interaction, plays an important role in surgical decision-making especially for bone-drilling. Existing surgical simulators provide either no haptic device or multipurpose haptic devices, which is difficult to reproduce the characteristics of surgical tool handling. In this paper, a custom-designed 3-DOF (pitch, yaw, radial) compact haptic interface for this surgery simulation is presented. It is dedicated to mimicking the touch sense of the surgical tools inserted through the nostril. Its main innovation is the mechanism design to maintain as much fidelity of the tool handling in the surgical training as in a real operation. The mechanism design is presented in detail as well as the kinematics and the force transmission. The mechanical characteristics of this haptic interface are also analyzed and presented.

sEMG-based prediction of human lower extremity movements by using a dynamic recurrent neural network

In this paper, a novel robust nonlinear model is proposed to predict human lower extremity motion based on the multi-channel surface electromyography (sEMG) signals. The prediction model is established by a data-driven dynamic recurrent neural network. The sEMG signals acquired from human lower extremity muscles are used as the inputs of the prediction model. The outputs of the model are joint angles of hip, knee and ankle. Different from the traditional feedforward network structure, this model has several feedback loops, thus it can take advantage of the output feedback information. To validate the effectiveness of the proposed method, five able-bodied people participated in the cycling exercises and relevant data were recorded in real time. The performance of the proposed prediction model is compared to those of the feedforward neural network with augmented inputs (FFNNAI) for the motion prediction accuracy and robustness. The results show that the proposed method provides acceptable performance which is clearly better than the FFNNAI-based approach under different experimental schemes.

Preliminary study on Wilcoxon-norm-based robust extreme learning machine

The fact that the linear estimators using the rank-based Wilcoxon approach in linear regression problems are usually insensitive to outliers is known in statistics. Outliers are the data points that differ greatly from the pattern set by the bulk of the data. Inspired by this fact, Hsieh et al. introduced the Wilcoxon approach into the area of machine learning. They investigated four new learning machines, such as Wilcoxon neural network (WNN), and developed four gradient descent based backpropagation algorithms to train these learning machines. The performances of these machines are better than ordinary nonrobust neural networks in outliers exist tasks. However, it is hard to balance the learning speed and the stability of these algorithms which is inherently the drawback of gradient descent based algorithms. In this paper, a new algorithm is used to train the output weights of single-layer feedforward neural networks (SLFN) with input weights and biases being randomly chosen. This algorithm is called Wilcoxon-norm based robust extreme learning machine or WRELM for short.