Su-Lin Lee

Emerging Robotic Platforms for Minimally Invasive Surgery

Recent technological advances in surgery have resulted in the development of a range of new techniques that have reduced patient trauma, shortened hospitalization, and improved diagnostic accuracy and therapeutic outcome. Despite the many appreciated benefits of minimally invasive surgery (MIS) compared to traditional approaches, there are still significant drawbacks associated with conventional MIS including poor instrument control and ergonomics caused by rigid instrumentation and its associated fulcrum effect. The use of robot assistance has helped to realize the full potential of MIS with improved consistency, safety and accuracy. The development of articulated, precision tools to enhance the surgeons dexterity has evolved in parallel with advances in imaging and human-robot interaction. This has improved hand-eye coordination and manual precision down to micron scales, with the capability of navigating through complex anatomical pathways. In this review paper, clinical requirements and technical challenges related to the design of robotic platforms for flexible access surgery are discussed. Allied technical approaches and engineering challenges related to instrument design, intraoperative guidance, and intelligent human-robot interaction are reviewed. We also highlight emerging designs and research opportunities in the field by assessing the current limitations and open technical challenges for the wider clinical uptake of robotic platforms in MIS.

From medical images to minimally invasive intervention: Computer assistance for robotic surgery

Minimally invasive surgery has been established as an important way forward in surgery for reducing patient trauma and hospitalization with improved prognosis. The introduction of robotic assistance enhances the manual dexterity and accuracy of instrument manipulation. Further development of the field in using pre- and intra-operative imaging guidance requires the integration of the general anatomy of the patient with clear pathologic indications and geometrical information for preoperative planning and intra-operative manipulation. It also requires effective visualization and the recreation of haptic and tactile sensing with dynamic active constraints to improve consistency and safety of the surgical procedures. This paper describes key technical considerations of tissue deformation tracking, 3D reconstruction, subject-specific modeling, image guidance and augmented reality for robotic assisted minimally invasive surgery. It highlights the importance of adapting preoperative surgical planning according to intra-operative data and illustrates how dynamic information such as tissue deformation can be incorporated into the surgical navigation framework. Some of the recent trends are discussed in terms of instrument design and the usage of dynamic active constraints and human-robot perceptual docking for robotic assisted minimally invasive surgery.

A Simulation Environment for Subject-Specific Radio Channel Modeling in Wireless Body Sensor Networks

This paper presents a simulation environment, based on the parallel finite-difference time-domain (FDTD) method, for subject-specific radio channel modeling in wireless body sensor networks (WBSNs). The simulation environment takes into account realistic antenna radiation patterns in channel modeling to analyze their effects on WBSNs. The proposed simulation tool is applied to a study of body communication channels in a hospital environment and the results are validated by site measurement. It is found from our study that radio channel characteristics in WBSNs are subject specific and associated with human genders and body mass indices (BMIs).

Learning-Based Modeling of Endovascular Navigation for Collaborative Robotic Catheterization

Despite rapid growth of robot assisted catheterization in recent years, most current platforms are based on master-slave designs with limited operator-robot collaborative control and automation. Under this setup, information concerning subject specific behavior and context-driven manoeuvre is not re-utilized for subsequent intervention. For endovascular catheterization, the robot itself is designed with little consideration of underlying skills and associated motion patterns. This paper proposes a learning-based approach for generating optimum motion trajectories from multiple demonstrations of a catheterization task such that it can be used for automating catheter motion within a collaborative setting. Motion models are generated from experienced manipulation of a catheterization procedure and replicated using a robotic catheter driver to assist inexperienced operators. Catheter tip motions of the automated approach are compared against the manual training sets for validating the proposed framework. The results show significant improvements in the quality of catheterization, which facilitate the design of hands-on collaborative robots that make full use of the natural skills of the operators.

A whole body statistical shape model for radio frequency simulation

The development of ultra low power wireless sensors for customized wearable and implantable medical devices requires patient specific models for radio frequency simulation to understand wave propagation in the body. In practice, the creation of a patient specific whole-body model is difficult and time consuming to create. It is therefore necessary to establish a method for studying a population in a statistical manner. In this paper, we present a statistical shape model for the whole body for RF simulation. It is built from 10 male and 10 female subjects of varying size and height. This model has the ability to instantiate a new surface mesh with the parameters allowed by the training set. This model would provide shapes of varying sizes for studies, without the requirement of obtaining subject specific whole body models. Results from finite-differences time-domain simulation are presented on the extreme shapes from the model and demonstrate the need for a full understanding of the range in body shapes.

Assessment of navigation cues with proximal force sensing during endovascular catheterization

Despite increased use of robotic catheter navigation systems for endovascular intervention procedures, current master-slave platforms have not yet taken into account dexterous manipulation skill used in traditional catheterization procedures. Information on tool forces applied by operators is often limited. A novel force/torque sensor is developed in this paper to obtain behavioural data across different experience levels and identify underlying factors that affect overall operator performance. The miniature device can be attached to any part of the proximal end of the catheter, together with a position sensor attached to the catheter tip, for relating tool forces to catheter dynamics and overall performance. The results show clear differences in manipulation skills between experience groups, thus providing insights into different patterns and range of forces applied during routine endovascular procedures. They also provide important design specifications for ergonomically optimized catheter manipulation platforms with added haptic feedback while maintaining natural skills of the operators.

Simultaneous catheter and environment modeling for Trans-catheter Aortic Valve Implantation

This paper proposes a new vasculature reconstruction and catheter modeling scheme based on data fusion from intravascular ultrasound (IVUS) imaging, electromagnetic (EM) tracking and shape sensing for trans-femoral Transcatheter Aortic Valve Implantation (TAVI). The system is suitable for obtaining inner cross sectional images of the aorta with an IVUS probe, reconstructing its 3D virtual model using sensor fusion of the corresponding pose information of IVUS probe from an electromagnetic (EM) sensor, as well as reconstructing the catheter shape based on optical fibers with Fiber Bragg Grating (FBG) sensors. A hybrid probe consisting of an IVUS sensor, an EM sensor and an optical shape sensor has been created and tested on in-vitro silicone aortic phantoms. A practical image processing method based on the gradient vector flow (GVF) snake has been proposed, followed by fusion with pose information from an EM sensor for the anatomical model reconstruction. Demonstration of the proposed method was performed on two aortic phantoms. Preliminary results show how the catheter shape reconstruction is realized by the shape sensor. The proposed method could facilitate intra-operative surgical guidance for valve alignment, improve the precision for positioning, reduce the time of the TAVI procedure, minimize the use of contrast agent, and assess the status of the deployed valve after surgery.

A General Framework for Context-Specific Image Segmentation Using Reinforcement Learning

This paper presents an online reinforcement learning framework for medical image segmentation. The concept of context-specific segmentation is introduced such that the model is adaptive not only to a defined objective function but also to the users intention and prior knowledge. Based on this concept, a general segmentation framework using reinforcement learning is proposed, which can assimilate specific user intention and behavior seamlessly in the background. The method is able to establish an implicit model for a large state-action space and generalizable to different image contents or segmentation requirements based on learning in situ. In order to demonstrate the practical value of the method, example applications of the technique to four different segmentation problems are presented. Detailed validation results have shown that the proposed framework is able to significantly reduce user interaction, while maintaining both segmentation accuracy and consistency.

Dynamic shape instantiation for intra-operative guidance

Primary liver cancer and oligometastatic liver disease are one of the major causes of mortality worldwide and its treatment ranges from surgery to more minimally invasive ablative procedures. With the increasing availability of minimally invasive hepatic approaches, a real-time method of determining the 3D structure of the liver and its location during the respiratory cycle is clinically important. However, during treatment, it is difficult to acquire images spanning the entire 3D volume rapidly. In this paper, a dynamic 3D shape instantiation scheme is developed for providing subject-specific optimal scan planning. Using only limited planar information, it is possible to instantiate the entire 3D geometry of the organ of interest. The efficacy of the proposed method is demonstrated with both detailed numerical simulation and a liver phantom with known ground-truth data. Preliminary clinical application of the technique is evaluated on a patient group with metastatic liver tumours.

A subject-specific radio propagation study in wireless body area networks

This paper presents a study of subject-specific radio channels in wireless body area networks (WBANs). The simulation tool is based on a parallel finite-difference timedomain method (FDTD) and is well suited to model radio propagations around complex, inhomogeneous objects including human bodies in WBAN. It is found from our study that radio channel characteristics in WBAN are subject specific when the received signal mainly contains the contribution of creeping waves around the human body.