Kyaw Kyar Toe

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subjects self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Quantification of coronary artery Stenosis by Area Stenosis from cardiac CT angiography.

Non-invasive cardiac computed tomography angiography (CTA) is widely used to assess coronary artery stenosis and give clinical decision-making support to clinicians. The severity of stenosis lesion is commonly graded by a range of percent Diameter Stenosis (DS), which can introduce false positive diagnoses or over-estimation, triggering unnecessary further procedures. In this paper, a system and the associate methods to quantify stenosis by the percent Area Stenosis (AS) from cardiac CTA is presented. In the process, coronary artery tree is segmented and the centerline is extracted by Hessian filtering and the minimal path method. After a serial of 2D cross-sectional artery images along the artery centerline are obtained, lumen areas are segmented by ellipse-fitting with deformable models, and consequently to compute the lesions AS. Experimental results on 5 CTA data sets show that compared to DS, AS better correlates to the reference standard for stenosis quantification, suggesting the efficacy of the proposed system.Non-invasive cardiac computed tomography angiography (CTA) is widely used to assess coronary artery stenosis and give clinical decision-making support to clinicians. The severity of stenosis lesion is commonly graded by a range of percent Diameter Stenosis (DS), which can introduce false positive diagnoses or over-estimation, triggering unnecessary further procedures. In this paper, a system and the associate methods to quantify stenosis by the percent Area Stenosis (AS) from cardiac CTA is presented. In the process, coronary artery tree is segmented and the centerline is extracted by Hessian filtering and the minimal path method. After a serial of 2D cross-sectional artery images along the artery centerline are obtained, lumen areas are segmented by ellipse-fitting with deformable models, and consequently to compute the lesions AS. Experimental results on 5 CTA data sets show that compared to DS, AS better correlates to the reference standard for stenosis quantification, suggesting the efficacy of the proposed system.

Synchronous simulation for deformation of liver and gallbladder with stretch and compression compensation

One challenge in surgical simulation is to design stable deformable models to simulate the dynamics of organs synchronously. In this paper, we develop a novel mass-spring model on the tetrahedral meshes for soft organs such as the liver and gallbladder, which can stably deform with large time steps. We model the contact forces between the organs as a kind of forces generated by the tensions of repulsive springs connecting in between the organs. The simulation system couples a pair of constraints on the length of springs with an implicit integration method. Based on the novel constraints, our simulator can efficiently preserve the volumes and geometric properties of the liver and gallbladder during the simulation. The numerical examples demonstrate that the proposed simulation system can provide realistic and stable deformable results.

Modeling and Simulation of Soft Tissue Deformation

A stable and accurate deformable model to simulate the deformation of soft tissues is a challenging area of research. This paper describes a soft tissue simulation method that can deform multiple organs synchronously and interact with virtual surgical instruments accurately. The model we used in our method is a multi-organ system by point masses and springs. The organs that anatomically connect to each other are jointed together by high stiffness springs. Here we propose a volume preserved mass-spring model for simulation of soft organ deformation. It does not rely on any direct constraint on the volume of tetrahedrons, but rather two constraints on the length of springs and the third constraint on the direction of springs. To provide reliable interaction between the soft tissues and kinematic instruments we incorporate the position-based attachment to accurately move the soft tissue with the tools. Experiments have been designed for evaluation of our method on porcine organs. Using a pair of freshly harvested porcine liver and gallbladder, the real organ deformation is CT scanned as ground truth for evaluation. Compared to the porcine model, our model achieves a mean absolute error 1.5024 mm on landmarks with a overall surface error 1.2905 mm for a small deformation the deformation of the hanging point is 49.1091 mm and a mean absolute error 2.9317 mm on landmarks with a overall surface error 2.6400 mm for a large deformation the deformation of the hanging point is 83.1376 mm. The change of volume for the two deformations are limited to 0.22% and 0.59%, respectively. Finally, we show that the proposed model is able to simulate the large deformation of the liver and gallbladder system in real-time calculations.

Neural representations of movement intentions during brain-controlled self-motion

Using a brain-machine interface (BMI), a non-human primate (NHP) was trained to control a mobile robotic platform in real time using spike activity from the motor cortex, enabling self-motion through brain-control. The decoding model was initially trained using neural signals recorded when the NHP controlled the platform using a joystick. Using this decoding model, we compared the performance of the BMI during brain control with and without the use of a dummy joystick, and found that the success ratio dropped by 40% and time taken increased by 45% when the dummy joystick was removed. Performance during full brain control was only restored after a recalibration of the decoding model. We aimed to understand the differences in the underlying neural representations of movement intentions with and without the use of a dummy joystick, and showed that there were significant changes in both directional tuning, as well as global firing rates. These results indicate that the strategies used by the NHP for self-motion were different depending on whether a dummy joystick was present. We propose that a recalibration of the decoding model is an important step during the implementation of a BMI system for self-motion.

Motor cortical adaptation induced by closed-loop BCI

Brain-computer interface (BCI) could help disabled patients with a broken neural pathway from brain to limbs restore movements by directly exploiting brain signals. Current laboratory BCIs on nonhuman primates (NHPs) were usually started from open-loop hand control (HC) setup for calibration and training, and then progressed to closed-loop brain control (BC) setup without using natural limbs. Successful transition from HC to BC necessitated motor leaning and neural plasticity which might involve the cortical adaptation induced by learning BCI. One useful strategy is to design neural feedback procedure to assist such adaptation and learning. We present an intracortical BCI on NHP with our designed feedback training procedure. In particular, we showed the motor cortical adaptation in terms of single neuron spiking activity in vivo during the closed-loop motor learning induced by our designed feedback training procedure. This experimental work can complement the existing theoretical modeling works on such closed-loop learning process.

REAL-TIME SIMULATION OF BLEEDING AND SMOKE EFFECTS IN VIRTUAL REALITY ENVIRONMENT

This work presents a method for the generation of realistic bleeding effects in a real-time virtual simulation environment. This method is used in, but not limited to, the context of a virtual surgical simulator for laparoscopic surgery. In the proposed method, we employed a particlebased approach where quad-shaped decals with blood colored texture are used to simulate individual blood drops that flow out of an organ due to accidental contact with surgical tools during surgery. The path of the blood flow is computed on-the-fly such that it adapts in realtime to the organ deformation and the effect of gravity. Mechanisms are designed to emulate the behavior in blood emission and blood flow such as squirting at the wound and attrition due to friction. In addition, by using customized shader graphics, we are able to achieve a 3D curved-contour visual effect simulating the bumpiness of each individual blood droplet that adapts to varying lighting conditions. Our results indicate that we are able to achieve good visual realism, adaptive behavioural performance and modest computational footprint.

Adaptive decoding using local field potentials in a brain-machine interface

Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model. We implemented a BMI system in a nonhuman primate model that allows brain-controlled movement of a robotic platform. Using this system, we showed that LFP signals alone can be used for decoding in a closed-loop brain-controlled BMI. Further, we performed offline analysis to assess the potential implementation of an adaptive decoding method that does not presume knowledge of the target location. Our results show that with periodic signal and channel selection adaptation, decoding accuracy using LFP alone can be improved by between 5-50%. These results demonstrate the feasibility of implementing unsupervised adaptive methods during asynchronous decoding of LFP signals for long-term usage in a BMI system.

A virtual surgical training system that simulates cutting of soft tissue using a modified pre-computed elastic model

This work presents a surgical training system that incorporates cutting operation of soft tissue simulated based on a modified pre-computed linear elastic model in the Simulation Open Framework Architecture (SOFA) environment. A precomputed linear elastic model used for the simulation of soft tissue deformation involves computing the compliance matrix a priori based on the topological information of the mesh. While this process may require a few minutes to several hours, based on the number of vertices in the mesh, it needs only to be computed once and allows real-time computation of the subsequent soft tissue deformation. However, as the compliance matrix is based on the initial topology of the mesh, it does not allow any topological changes during simulation, such as cutting or tearing of the mesh. This work proposes a way to modify the pre-computed data by correcting the topological connectivity in the compliance matrix, without re-computing the compliance matrix which is computationally expensive.

Surgical Simulation Robot with Haptics and Friction Compensation

Haptic feedback brings a surgical simulator closer to real surgery. However, friction in surgical simulators hardware affects its performance significantly. We introduce a surgical simulation robot with roller mechanism for laparoscopic surgical simulation. Roller mechanism is implemented in a constrained space to reduce the friction. Motion based friction cancellation method is also applied to further mitigate the friction effects. Comparing with the same surgical simulation robot without roller mechanism, the one with roller mechanism reduces friction by 32.86i?ź% and 38.87i?ź% on two motion directions, and the motion based friction cancellation method can mitigate the friction effect by 49.46i?ź% and 62.08i?ź% on the two motion directions.