Tie Hu

Characterization of Soft-Tissue Material Properties: Large Deformation Analysis

The biomechanical properties of soft tissue derived from experimental measurements are critical for developing a reality-based model for minimally invasive surgical training and simulation. In our research, we focus on developing a biomechanical model of the liver under large tissue deformation. This paper presents the experimental apparatus, experimental data, and formulations to model the experimental data through finite element simulation and also compare it with the hyperelastic models in the literature. We used tissue indentation equipment to characterize the biomechanical properties of the liver and compared the local effective elastic modulus (LEM) derived from experimental data with that from plane stress and plane strain analysis in ABAQUS. Our results show that the experimentally derived LEM matches closely with that derived from ABAQUS in plane stress and plane strain analysis and the Ogden hyperelastic model for soft tissue.

A Novel Approach to Robotic Cardiac Surgery Using Haptics and Vision

Cardiovascular disease is one of the leading causes of death in the United States and also a major disease nationwide. Over 700,000 coronary artery bypass graft (CABG) procedures are performed annually all around the world, of which 350,000 are performed in the United States. The use of mechanical stabilizers to isolate and immobilize the surface region of the heart is not without its limitations such as hemodynamic deterioration, and arrythmia induction requiring inotropic support. Consequently, the use of mechanical stabilizers leads to a poor immobilization of the surgical field in spite of significant forces of traction and retraction used with these devices. The primary goal of this research is to develop effective haptic (sense of touch) and visual servoing methods with the long-term goal of eliminating the need for mechanical stabilizers and extracorporeal support for CABG procedures. We present in this paper the results from our initial work in the area of tracking a deformable membrane using vision and providing haptic feedback to the user, based on the visual information through the vision hardware and the material properties of the membrane. In our first experiment, we track the deformation of a rubber membrane in real-time through stereovision while providing haptic feedback to the user interacting with the reconstructed membrane through the PHANToM haptic device. In the second experiment, we verify the ability of our vision system to track a point on a surface undergoing a complex 3D motion.

Soft-tissue material properties under large deformation: strain rate effect

Biomechanical model of soft tissue derived from experimental measurements is critical for developing a reality-based model for minimally invasive surgical training and simulation. In our research, we have focused on developing a biomechanical model of the liver with the ultimate goal of using this model for local tool-tissue interaction tasks and providing feedback to the surgeon through a haptic display. We are interested in finding the local effective elastic modulus (LEM) of the liver tissue under different strain rates. We have developed a tissue indentation equipment for characterizing the biomechanical properties of the liver and compared the local effective elastic modulus (LEM) derived from experimental data with plane stress, plane strain, and axisymmetric element types in ABAQUS under varying strain rates. Our results show that the experimentally derived local effective modulus matches closely with the plane stress analysis in ABAQUS.

Real-Time Haptic Feedback in Laparoscopic Tools for Use in Gastro-Intestinal Surgery

One of the limitations of current surgical robots used in surgery is the lack of haptic feedback. While current surgical robots improve surgeon dexterity, decrease tremor, and improve visualization, they lack the necessary fidelity to help a surgeon characterize tissue properties for improving diagnostic capabilities. Our work focuses on the development of tools and software that will allow haptic feedback to be integrated in a robot-assisted gastrointestinal surgical procedure. In this paper, we have developed several tissue samples in our laboratory with varying hardness to replicate real-tissues palpated by a surgeon in gastrointestinal procedures. Using this tissue, we have developed a novel setup whereby the tactile feedback from the laparoscopic tool is displayed on the PHANToM haptic interface device in real-time. This is used for tissue characterization and classification. Several experiments were performed with different users and they were asked to identify the tissues. The results demonstrate the feasibility of our approach.

A Biomechanical Model of the Liver for Reality-Based Haptic Feedback

Biomechanical model of soft tissue for remote probing based on observed experimental data is critical for developing a reality-based model for minimally invasive surgical training and simulation. In our research, we have focused on developing a biomechanical model of the liver with the ultimate goal of using this model for local tool-tissue interaction tasks and providing feedback through a haptic display. We have designed and developed tissue indentation equipment for characterizing the biomechanical properties of the liver and formulated a hybrid nonlinear model that is valid in both low strain and high strain regions. The pig liver is simplified as the incompressible, isotropic, and homogeneous elastic material. This model will be the basis for a finite element model for the pig liver.

Measuring grasping and cutting forces for reality-based haptic modeling

Abstract The modeling of grasping and cutting in surgery are two fundamental tasks that must be achieved for the development of a reality-based haptic interface in robot-assisted surgery. Currently, the lack of these models with soft tissue has limited the accuracy of such interfaces in surgery. As a result, we have taken the first steps in realizing soft tissue models through the development of an automated laparoscopic grasper and tissue cutting equipment to characterize grasping and cutting tasks in minimally invasive surgery (MIS). The grasper is capable of generating force feedback that can be felt through a haptic interface device thereby allowing a user to feel the stiffness of the tissue that is being grasped. The cutting equipment employs a surgical scalpel attached to a six-axis force/torque sensor to measure the forces during cutting. The scalpel follows a linear motion created by a DC motor and leadscrew assembly.

Combining haptic and visual servoing for cardiothoracic surgery

The primary goal of this research is to develop effective haptic and visual servoing methods, with the eventual goal of eliminating the need for mechanical stabilizers in a coronary artery bypass graft procedure by presenting a stationary operative site to the surgeon performing the procedure using haptic and visual feedback. We present the results from our initial work in the area of tracking a deformable membrane using vision and providing haptic feedback to the user based on the vision information and the material properties of the membrane. In our first experiment, we track the deformation of a rubber membrane in real-time through stereo vision while providing haptic feedback to the user interacting with the reconstructed membrane through the PHANToM haptic device. In the second experiment, we verify the ability of our vision system to track a point on a surface undergoing a complex 3D motion.

Direct and inverse problem models for large soft-tissue deformation: Application to haptic feedback in surgical simulation

Biomechanical model of soft tissue derived from experimental measurements is critical to develop a reality-based model for minimally invasive surgical training and simulation. We have focused on developing a biomechanical model of the liver with the ultimate goal of using this model for local tool-tissue interaction tasks and providing feedback to the surgeon through a haptic (sense of touch) display. It is of interest to develop a model of the soft tissue through probing experiments. An experimental apparatus was developed to perform both large probe compression test and the tissue probing test. The specimens were compressed to attain 30% nominal strain using a range of probing speeds (0.1016 mm/sec, 5.08 mm/sec, 12.70 mm/sec and 25.40 mm/sec). The compressive force-displacement curves were monotonic but highly nonlinear. Models consistent with experimental data were developed to characterize the deformation resistance of the soft tissue. Removing the assumption of incompressibility, an inverse-problem model can computationally determine the LEEM with axisymmetric finite element analysis. The sensitivity of LEEM on the tissues compressibility (Poissons ratio) was presented. Additionally, the variation of LEEM of pig liver with the probing speed was revealed. These results can be used to model soft-tissue deformation under varying probing speed by a surgical tool

Modeling In vivo Soft Tissue Probing

A biomechanical model of in vivo soft tissue derived from experimental measurements is critical for developing a reality-based model for minimally invasive surgical training and simulation. In our research, we have been focusing on developing a biomechanical model of the liver with the ultimate goal of using this model for local tool-tissue interaction tasks and providing feedback to the surgeon through a haptic (sense of touch) display. In this paper, we present our approach for characterizing the nonlinear property of soft tissue in vivo under large deformation. We developed an experimental method for in vivo soft tissue test, and an axisymmetric finite element model to obtain the local effective elastic modulus (LEEM) of the tissue. A microcontroller-based portable probe was developed to measure the force and displacement in vivo of the pig liver tissue undergoing large deformation. The probe indented the liver up to 40% of its nominal thickness at a speed of 1.5 mm/sec. Based on the experimental force and displacement data, we obtained the LEEM by an inverse finite element method