Real-time Biomechanical Modeling for Intraoperative Soft Tissue Registration

Computer assisted surgery systems intraoperatively support the surgeon by providing information on the location of hidden risk and target structures during surgery. However, soft tissue deformations make intraoperative registration (and thus intraoperative navigation) difficult. In this work, a novel, biomechanics based approach for real-time soft tissue registration from sparse intraoperative sensor data such as stereo endoscopic images is presented to overcome this problem

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance

Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations. Purpose. In this work, we particularly look at mitral valve reconstruction (MVR) surgery, which is to re-establish the functionality of an incompetent mitral valve (MV) through implantation of an artificial ring that reshapes the valvular morphology. We aim at supporting MVR by providing surgeons with biomechanical FEM-based MVR surgery simulations that enable them to assess the simulated behavior of the MV after an MVR. However, according to the above requirements, such surgery simulation is really beneficial to surgeons only if it is patient-specific, surgical expert knowledge-based, comprehensive in terms of the underlying model and the patient’s data, and if its setup and execution is fully automated and integrated into the surgical treatment workflow. Methods. This PhD work conducts research on simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. First, we derive a biomechanical MV/MVR model and develop an FEM-based MVR surgery simulation using the FEM software toolkit HiFlow3. Following, we outline the functionality and features of the Medical Simulation Markup Language (MSML) and how it simplifies the biomechanical modeling workflow. It is then detailed, how, by means of the MSML and a set of dedicated MVR simulation reprocessing operators, patient-individual medical data can comprehensively be analyzed and processed in order for the fully automated setup of MVR simulation scenarios. Finally, the presented work is integrated into the cognitive system architecture of the joint research project Cognition-Guided Surgery. We particularly look at its semantic knowledge and data infrastructure as well as at the setup of its cognitive software components, which eventually facilitate cognition-guidance and patient-specifity for the overall simulation-enhanced MVR assistance pipeline. Results and Discussion. We have proposed and implemented, for the first time, a prototypic system for simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. The overall system was evaluated in terms of functionality and performance. Through its cognitive, data-driven pipeline setup, medical patient data and surgical information is analyzed and processed comprehensively, efficiently and fully automatically, and the hence set-up simulation scenarios yield reliable, patient-specific MVR surgery simulation results. This indicates the system’s usability and applicability. The proposed work thus presents an important step towards a simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance, and can – once operative – be expected to significantly enhance MVR surgery. Concluding, we discuss possible further research contents and promising applications to build upon the presented work

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces

Capacity planning and stochastic scheduling in grid computing environments

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Research on real-time physics-based deformation for haptic-enabled medical simulation

This study developed a multiple effective visuo-haptic surgical engine to handle a variety of surgical manipulations in real-time. Soft tissue models are based on biomechanical experiment and continuum mechanics for greater accuracy. Such models will increase the realism of future training systems and the VR/AR/MR implementations for the operating room

Meshless Mechanics and Point-Based Visualization Methods for Surgical Simulations

Computer-based modeling and simulation practices have become an integral part of the medical education field. For surgical simulation applications, realistic constitutive modeling of soft tissue is considered to be one of the most challenging aspects of the problem, because biomechanical soft-tissue models need to reflect the correct elastic response, have to be efficient in order to run at interactive simulation rates, and be able to support operations such as cuts and sutures. Mesh-based solutions, where the connections between the individual degrees of freedom (DoF) are defined explicitly, have been the traditional choice to approach these problems. However, when the problem under investigation contains a discontinuity that disrupts the connectivity between the DoFs, the underlying mesh structure has to be reconfigured in order to handle the newly introduced discontinuity correctly. This reconfiguration for mesh-based techniques is typically called dynamic remeshing, and most of the time it causes the performance bottleneck in the simulation. In this dissertation, the efficiency of point-based meshless methods is investigated for both constitutive modeling of elastic soft tissues and visualization of simulation objects, where arbitrary discontinuities/cuts are applied to the objects in the context of surgical simulation. The point-based deformable object modeling problem is examined in three functional aspects: modeling continuous elastic deformations with, handling discontinuities in, and visualizing a point-based object. Algorithmic and implementation details of the presented techniques are discussed in the dissertation. The presented point-based techniques are implemented as separate components and integrated into the open-source software framework SOFA. The presented meshless continuum mechanics model of elastic tissue were verified by comparing it to the Hertzian non-adhesive frictionless contact theory. Virtual experiments were setup with a point-based deformable block and a rigid indenter, and force-displacement curves obtained from the virtual experiments were compared to the theoretical solutions. The meshless mechanics model of soft tissue and the integrated novel discontinuity treatment technique discussed in this dissertation allows handling cuts of arbitrary shape. The implemented enrichment technique not only modifies the internal mechanics of the soft tissue model, but also updates the point-based visual representation in an efficient way preventing the use of costly dynamic remeshing operations

Towards the application of multi-DOF EMG-driven neuromusculoskeletal modeling in clinical practice: methodological aspects

New methods able to assess the individual ability of patients to generate motion and adaptation strategies are increasingly required for clinical applications aiming at recovering motor functions. Indeed, more effective rehabilitation treatments are designed to be personalized on the subject capabilities. In this context, neuromusculoskeletal (NMS) models represent a valuable tool, as they can provide important information about the unique anatomical, neurological, and functional characteristics of different subjects, through the computation of human internal variables, such as muscle activations, muscle forces, joint contact forces and moments. A first possible approach is to estimate these values using optimization-based NMS models. However, these models require to make assumptions on how the muscles contribute to the observed movement. More promising are instead NMS models driven by electromyographic signals (EMG), which use experimentally recorded signals that can be considered a direct representation of the subject motor intentions. This allows to account for the actual differences in an individual neuromuscular control system, without making any preliminary assumptions. Therefore these models have the potentialities to provide the level of personalization that is essential for applications in the clinical field. Although EMG-driven NMS models have been investigated in the literature, even for clinical purposes, they are mostly limited to one degree of freedom (DOF), and consider only the muscles spanning that DOF. Additionally, despite the promising results, they are still not introduced in the clinical practice; the main reason possibly being their complexity, that makes them not usable in clinical context, where standard and reliable procedures are required. The importance of EMG-driven NMS modeling for clinical applications would be even higher with the availability of multi-DOF models, as impairments usually compromise multiple joints. Nevertheless, even if a first multi-DOF EMG-driven NMS model for the lower limbs has been recently introduced in literature, its even greater complexity makes more difficult an analysis of its applicability in the clinical field. This work represents a first effort towards a critical analysis of multi-DOF EMG-driven NMS models to evaluate their possible use in clinical practice. To achieve this objective, several issues and limitations have been addressed. In the specific, the attention has been focused on two aspects: (i) making the methodology usable, to foster its adoption by multiple laboratories and research groups, and to facilitate sensitivity analyses required to assess its accuracy; (ii) highlighting the effects of some methodological aspects related to data acquisition and processing, and evaluating their impact on the accuracy of estimated parameters and muscle forces. This analysis is even more important for multi-DOF EMG-driven NMS model as it is still not present in the literature. To accomplish the first goal, a software tool (MOtoNMS) has been developed and it is freely available for the research community. It is a complete, flexible, and user-friendly tool that allows to automatically process experimental motion data from different laboratories in a transparent and repeatable way, for their subsequent use with neuromusculoskeletal modeling software. MOtoNMS generalizes data processing methods across laboratories, and simplifies and speeds up the demanding data elaboration workflow. This simplification represents an indispensable step towards an actual translation of NMS methods in clinical practice. The second part of the work has been, instead, dedicated to analyze the impact on model parameters and muscle forces prediction of different techniques for EMG data collection and processing that are feasible for clinical settings, in particular concentrating on EMGs normalization. Indeed, moving EMG-driven NMS modeling towards clinical applications that deal with multiple DOFs requires to carefully consider subject's motor limitations due to his/her mobility impairments. This results in a rethinking about the methodologies for data acquisition and processing. Therefore, the impact of using only data from walking trials on both calibration of model parameters and computing the maximum EMG values needed for the normalization step, has been assessed with two case studies. Moreover, a protocol for the collection of maximum voluntary contractions has been proposed. This protocol is suitable for multiple DOFs applications involving patients with reduced motor ability and it requires only low-cost and easy to acquire tools to make it applicable in any laboratory. The research proposed in this thesis provides tools to simplify the use of multi-DOF EMG-driven neuromusculoskeletal models and proposes analyses and procedures to evaluate the accuracy and reliability of the obtained results with the aim of pursuing clinical applications

Advancing clinical gait analysis through technology and policy

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 161-165).Quantitatively analyzing human gait biomechanics will improve our ability to diagnose and treat disability and to measure the effectiveness of assistive devices. Gait analysis is one technology used to analyze walking, but technical as well as economic, social, and policy issues hinder its clinical adoption. This thesis is divided into two parts that address some of these issues. Part I focuses on the role public policies have in advancing gait analysis. Through an analysis of gait analysis technologies, case studies of MRI and CT Angiography, and a high-level analysis of data standards used in gait analysis, it concludes that policies cannot directly create the institutional structures and the data standards required to advance gait analysis as a clinical diagnostic tool. Only through indirect means, such as research funding, can policies support the development of organizations to take ownership of gait analysis technologies. Part I also concludes that policies should not fund development of gait technologies but instead should fund research units working on data standards and accurate human body models. Part II focuses on a technical issue in gait analysis, namely, how to address uncertainties in joint moment calculations that occur from using different body segment inertial parameter estimation models. This is identified as a technical issue needing attention from our broader policy analysis in Part I. Using sensitivity studies of forward dynamics computer simulations coupled with an analysis of the dynamical equations of motion, Part II shows that joint moment variations resulting from different segment inertial parameters are significant at some parts of the gait cycle, particularly heel strike and leg swing.(cont.) It provides recommendations about which segment inertial parameters one should estimate more accurately depending on which joints and phases of the gait cycle one is interested in analyzing.by Junjay Tan.S.M.S.M.in Technology and Polic

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction