Proxy chain method and its application to scientific visualization

Journal ArticleWe present a method for combining multiple point-based constraints in haptic programming environments. Instead of using a single proxy point for haptic feedback, the method maintains a separate proxy for each constraint. The reaction force is computed by linking the proxies in a chain. Constraints are applied in sequential order, such that the proxy found in the current step becomes the probe for the next step in the chain. The advantage of the method over previous approaches is that the constraints are maintained precisely and the output is well-defined. We illustrate the method with examples from the domain of 3D scientific data visualization. Finally, we present the results of an experiment conducted to quantify the contribution of haptic guidance in two representative vector field exploration tasks

A Study of Velocity-Dependent JND of Haptic Model Detail

The study of haptics, or the sense of touch in virtual reality environments, is constantly looking for improvements in modeling with a high fidelity. Highly detailed models are desirable, but they often lead to slow processing times, which can mean a loss of fidelity in the force feedback sensations. Model compression techniques are critical to balancing model detail and processing time. One of the proposed compression techniques is to create multiple models of the same object but with different levels of detail (LOD) for each model. The technique hypothesizes that the human arm loses sensitivity to forces with the increase of its movement speed. This the compression technique determines which model to use based on the user's movement speed. This dissertation examines studies how the movement speed of the user affects the user's ability to sense changes in details of haptic models. Experiments are conducted using different haptic surfaces. Their levels of detail are changed while the subject interacts with them to mimic the effects of a multiresolution compression implementation. The tests focus on the subjects' ability to differentiate changes of the surfaces at each speed. The first experiment uses curved surfaces with multiple resolutions. This test observes the sensitivity of the user when the details on the surface are small. The results show that the subjects are more sensitive to changes of small details at a lower speed than higher speed. The second experiment measures sensitivity to larger features by using trapezoidal surfaces with different angles. The trapezoidal surfaces can be seen as a low-resolution haptic model with only two vertices, and changing the angles of the trapezoids is seen as changing the radii of curvature. With the same speed settings from the first experiment applied to the subjects, the sensitivity for changes in curvature is predicted to decrease with the increase of speed. However, the results of this experiment proved otherwise. The conclusions suggest that multiresolution designs are not a straightforward reduction of LOD, even though the movement speed does affect haptic sensitivity. The model's geometry should be taken into account when designing the parameters for haptic model compression. The results from the experiments provide insights to future haptic multiresolution compression designs

Estimating and understanding motion : from diagnostic to robotic surgery

Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis. Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery. Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data. We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution. Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets. We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinàmics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties. En particular, ens situem en l’àmbit mèdic en el qual la comprensió del cos humà, amb finalitats clíniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difícil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clínica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà, el del cor, a través de tres aplicacions: (i) estimació del moviment cardíac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica. En primer lloc, ens centrem en un tema principal en la imatge cardíaca, que és l’estimació del moviment cardíac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultraràpids i eines per al moviment d’imatges procedents de diverses àrees, com ara l’anàlisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardíac. La importància rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realístiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes. Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per reduir la càrrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecànica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elàstic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anàlisi estadístic, gràfic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardíaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronària sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això comporta que els cirurgians han de tractar amb un objectiu dinàmic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut a terme mitjançant conjunts de dades sintètiques i realistes. La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinàmic, especialment en escenaris clínics.Postprint (published version

Interactively Cutting and Constraining Vertices in Meshes Using Augmented Matrices

We present a finite-element solution method that is well suited for interactive simulations of cutting meshes in the regime of linear elastic models. Our approach features fast updates to the solution of the stiffness system of equations to account for real-time changes in mesh connectivity and boundary conditions. Updates are accomplished by augmenting the stiffness matrix to keep it consistent with changes to the underlying model, without refactoring the matrix at each step of cutting. The initial stiffness matrix and its Cholesky factors are used to implicitly form and solve a Schur complement system using an iterative solver. As changes accumulate over many simulation timesteps, the augmented solution method slows down due to the size of the augmented matrix. However, by periodically refactoring the stiffness matrix in a concurrent background process, fresh Cholesky factors that incorporate recent model changes can replace the initial factors. This controls the size of the augmented matrices and provides a way to maintain a fast solution rate as the number of changes to a model grows. We exploit sparsity in the stiffness matrix, the right-hand-side vectors and the solution vectors to compute the solutions fast, and show that the time complexity of the update steps is bounded linearly by the size of the Cholesky factor of the initial matrix. Our complexity analysis and experimental results demonstrate that this approach scales well with problem size. Results for cutting and deformation of 3D linear elastic models are reported for meshes representing the brain, eye, and model problems with element counts up to 167,000; these show the potential of this method for real-time interactivity. An application to limbal incisions for surgical correction of astigmatism, for which linear elastic models and small deformations are sufficient, is included

Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery

Virtual environments for medical training : graphic and haptic simulation of tool-tissue interactions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (leaves 122-127).For more than 2,500 years, surgical teaching has been based on the so called "see one, do one, teach one" paradigm, in which the surgical trainee learns by operating on patients under close supervision of peers and superiors. However, higher demands on the quality of patient care and rising malpractice costs have made it increasingly risky to train on patients. Minimally invasive surgery, in particular, has made it more difficult for an instructor to demonstrate the required manual skills. It has been recognized that, similar to flight simulators for pilots, virtual reality (VR) based surgical simulators promise a safer and more comprehensive way to train manual skills of medical personnel in general and surgeons in particular. One of the major challenges in the development of VR-based surgical trainers is the real-time and realistic simulation of interactions between surgical instruments and biological tissues. It involves multi-disciplinary research areas including soft tissue mechanical behavior, tool-tissue contact mechanics, computer haptics, computer graphics and robotics integrated into VR-based training systems. The research described in this thesis addresses many of the problems of simulating tool-tissue interactions in medical virtual environments. First, two kinds of physically based real time soft tissue models - the local deformation and the hybrid deformation model - were developed to compute interaction forces and visual deformation fields that provide real-time feed back to the user. Second, a system to measure in vivo mechanical properties of soft tissues was designed, and eleven sets of animal experiments were performed to measure in vivo and in vitro biomechanical properties of porcine intra-abdominal organs. Viscoelastic tissue(cont.) parameters were then extracted by matching finite element model predictions with the empirical data. Finally, the tissue parameters were combined with geometric organ models segmented from the Visible Human Dataset and integrated into a minimally invasive surgical simulation system consisting of haptic interface devices inside a mannequin and a graphic display. This system was used to demonstrate deformation and cutting of the esophagus, where the user can haptically interact with the virtual soft tissues and see the corresponding organ deformation on the visual display at the same time.by Jung Kim.Ph.D

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons