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

Real-time simulation of soft tissue deformation for surgical simulation

Surgical simulation plays an important role in the training, planning and evaluation of many surgical procedures. It requires realistic and real-time simulation of soft tissue deformation under interaction with surgical tools. However, it is challenging to satisfy both of these conflicting requirements. On one hand, biological soft tissues are complex in terms of material compositions, structural formations, and mechanical behaviours, resulting in nonlinear deformation characteristics under an external load. Due to the involvement of both material and geometric nonlinearities, the use of nonlinear elasticity causes a highly expensive computational load, leading to the difficulty to achieve the real-time computational performance required by surgical simulation. On the other hand, in order to satisfy the real-time computational requirement, most of the existing methods are mainly based on linear elasticity under the assumptions of small deformation and homogeneity to describe deformation of soft tissues. Such simplifications allow reduced runtime computation; however, they are inadequate for modelling nonlinear material properties such as anisotropy, heterogeneity and large deformation of soft tissues. In general, the two conflicting requirements of surgical simulation raise immense complexity in modelling of soft tissue deformation. This thesis focuses on establishment of new methodologies for modelling of soft tissue deformation for surgical simulation. Due to geometric and material nonlinearities in soft tissue deformation, the existing methods have only limited capabilities in achieving nonlinear soft tissue deformation in real-time. In this thesis, the main focus is devoted to the real-time and realistic modelling of nonlinear soft tissue deformation for surgical simulation. New methodologies, namely new ChainMail algorithms, energy propagation method, and energy balance method, are proposed to address soft tissue deformation. Results demonstrate that the proposed methods can simulate the typical soft tissue mechanical properties, accommodate isotropic and homogeneous, anisotropic and heterogeneous materials, handle incompressibility and viscoelastic behaviours, conserve system energy, and achieve realistic, real-time and stable deformation. In the future, it is projected to extend the proposed methodologies to handle surgical operations, such as cutting, joining and suturing, for topology changes occurred in surgical simulation

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions

Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie